April 2017 AI for Sales Forecasting & Sales Process Execution · AI for Sales Forecasting & Sales Process Execution How artificial intelligence provides more ... with data-driven

AI for Sales Forecasting & Sales Process Execution How artificial intelligence provides more direction for forecasting and more wins

April 2017

In this issue

Introduction 2

How “Not” to Forecast 3

How to Create & Manage Your Forecast ... So You Can Trust It 4

6 Tenets of an Accurate Sales Forecast 5

Research From Gartner: Market Guide for SaaS-Based Predictive Analytics Applications for B2B 7

2

Introduction

“You missed the number? Hey, no problem, you’ll

get it next quarter!” said no CEO or board member

ever!!!

Credibility in hitting the number is absolutely the

most important thing for a sales leader. And

these leaders that hit their number regularly,

don’t have it easy. It’s usually a grind with a mad

scramble at the end of the quarter to hit plus or

minus 5%. It doesn’t have to be such a painful

process.

Artificial intelligence for sales forecasting builds

trust. If you can trust what you see in the

pipeline and that your pipeline is filling at the

appropriate rate with qualified leads and

opportunities that fit your ideal customer profile,

you can have confidence that you’ll hit your

number.

Before I tell you what’s needed to have a forecast you

can trust – let’s look at how we create and manage a

forecast today.

3

How “Not” to Forecast

If you’re like most execs in sales or sales ops, you’re

armed only with a couple of reports from your

CRM and a spreadsheet. You list and manage the

opportunities by sales rep and by stage - then you

spend a lot of time in conversations with your sales

team. Eventually you contact them daily for status

updates. Conversations are great with the sales

team – especially on strategy to close deals, but it’s

such a waste of valuable time to do daily status calls

between manager and sales reps on the same deals.

You still end up with minimal visibility and knowledge

about deals. And this leaves very little time for selling,

coaching and strategizing – among other priorities.

When you print out the pipeline for the team – it

shows 3x to 4x the number. Questions you should ask

yourself. Can I rely on that 3x to 4x pipeline number?

How do I find out how good that pipeline really is? Can

I rely on my weighted average number? Is there an

easier way to get visibility into deals? What do I have to

do to get this forecast to where I can trust it?

Additional side effects that your current sales

forecasting process causes:

Because the current sales forecasting process is such

a time suck, you hardly have any time left for:

1. Coaching and developing the sales team

2. Work on maximizing conversions throughout each stage

3. Ramping new sales reps

4. Understanding the effects of my lead-gen effort

And by not spending enough time on these four things,

closing business and quota attainment suffers.

4

How to Create & Manage Your Forecast ...So You Can Trust It

Sales Automation for the Sales Forecast

Imagine if you had an expert coach that knew your sales

process and prompted you on a logical next step on a

deal that was still active but you were starting to neglect?

Or if all of your deals magically went to the appropriate

stage and milestone and gave you an accurate forecast.

These are examples of what automation can provide.

Automation is valuable because it saves a ton of time,

and it organizes and applies discipline and rigor without

the manual effort. With process, workflow, and pipeline

hygiene automation, an entire sales team will have a

lot more time for selling and more value-added activity.

These efforts are normally manual, time consuming and

error prone.

The Use of Artificial Intelligence

A.I. provides an order of magnitude of valuable

information and insights for sales forecasting. By

applying the disciplines of sales automation for sales

forecasting, data can be analyzed and insights derived.

For example, insights can alert you to whether you have

enough pipeline to hit the number this quarter or next

quarter, and it can tell you what to do about it, such as

how much to increase Average Selling Price, or increase

lead gen efforts or conversion rates.

Do not use BI tools for sales analysis!

Leave the Business Intelligence tools for accounting,

HR or other departments where the data is more

manageable. Using BI against untamed data in the CRM

is almost as bad as using the spreadsheet. BI tools in the

sales department usually end up as great looking graphs

displaying insufficient and inaccurate data regurgitated

from the CRM. These tools lack the automation of a

sales forecasting application and the artificial intelligence

to keep the pipeline with clean hygiene, realistic deals

and accuracy.

5

6 Tenets of an Accurate Sales Forecast

So, how do you apply sales automation and AI to your sales forecast?

The following are 6 strategies that can be utilized

through automation and A.I., that will enhance the

sales forecast as well as provide additional benefits to

increase win rates and quota attainment.

1. Prescribe rules for the sales process

Create rules for advancing and regressing leads and

opportunities as well as when and why to close them

as a loss. Keep everyone from management to the

sales rep on the same page about process rules.

Discipline around when to move a deal, why, and

where it belongs will keep things well organized and

consistent. Here is a great resource on how to build,

tweak or overhaul your sales process - How to Build a

Winning Sales Process Guidebook

2. Enforce the sales process

CSO Insights research shows a 23% increase in quota

attainment occurs when a rigorous sales process is

used. By understanding how a deal flows through the

funnel you’ll have accurate information on where deals

get stuck and conversion rates. This is important data-

driven coaching information. And it’s not enough to

just have a process. Many do, but it’s just written down

somewhere or a powerpoint print out is pinned up on a

cubical wall and therefore loosely adhered to.

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There needs to be reminders to the sales reps on the

next step that follows the appropriate sales process.

These steps have a time limit if done effectively.

Automation serves sales reps in this way - increasing

the likelihood of a sale.

3. Enforce pipeline hygiene

The bane of every sales manager and sales rep is

the current state of pipeline analytics. There are too

many deals that are not accurate, not up to date, in

the wrong stage or just don’t belong in the pipeline.

You don’t have to spend the hours scrutinizing deals

for hygiene or increase the coverage of pipeline to

compensate - you just need to have the disciplines of

hygiene to be aware of better quality deals with higher

probability of wins. You can solve this problem by

reading further in this document.

4. Make it easy to update the CRM

Sales reps need to provide their point of view on deals.

These status updates need to be in the CRM as soon

as possible after any meeting or communication with

prospects and customers.

5. Capture “the right” signals for Artificial Intelligence

Artificial intelligence can provide great insights,

but false signals could distort prescribed insights

for decisions. Quality signals such as emails from

prospects and meetings from the sales teams’

calendars are helpful to determine opportunity quality.

6. Analyze how deals flow through the funnel by rep and by time

Now that you have 1 through 5 of the strategies in

order, managers and sales reps have sufficient and

accurate data amplifying their ability to get real

insights. This guidebook explains how to analyze

performance and customize coaching to get the entire

team exceeding quota.

Summary

By applying automation and AI in this way, you’ll be

able to trust your sales forecast, while developing and

making your sales team better. No longer will you

wonder if the 3x or 4x pipeline coverage are really

comprised of good deals. Conversations that you are

having with the sales reps will make their way into

the CRM for better analysis and decisions. Ramp

time of sales reps will be quicker, providing faster

times to quota attainment. And you’ll have time along

with data-driven analytics to better coach the team.

And thank goodness - you can finally get rid of that

spreadsheet!

To learn how our customers are benefitting from these

strategies - click here.

To see how sales automation and artificial intelligence

can be applied to give you a consistent sales process

and accurate forecast with TopOPPS - request a demo.

Source: TopOPPS

7

Research From Gartner

Market Guide for SaaS-Based Predictive Analytics Applications for B2B

SaaS-based predictive analytics applications are

helping B2B salespeople and marketers improve win

rates, deal velocity and size. IT application leaders

must understand the dynamics of this emerging and

rapidly growing category and identify providers that

can support these business units.

Key Findings

■ The market for SaaS-based predictive analytics

applications is still small and nascent (Gartner

estimates its worth at $100 million to $150 million

by the end of 2016), but it offers a compelling ROI

potential that should lead to rapid growth within

the next two years.

■ Many vendors, particularly those that target

marketing (rather than sales) users, now offer broad

solution suites that address many different use

cases — from segmentation to account selection,

demand generation and upsell/cross-sell.

■ Applications are typically purchased using short-

term subscription contracts (two years or less),

and vendor churn at the end of contracts remains

high due to unrealized expectations and/or the

ease of switching.

■ While differentiation exists based on focus, go-to-

market strategy, integrations, functionality and/or

data sources, many vendors use similar messaging

and positioning, which causes confusion for buyers.

8

Recommendations

IT application leaders who support marketing and sales:

■ Help your marketing team by investigating

and adopting SaaS-based predictive analytics

applications to improve segmentation, account

selection, demand generation and lead scoring

to increase conversion rates and contributions to

pipeline and revenue.

■ Help your sales team by investigating and using

SaaS-based predictive analytics applications to

improve forecasting, pipeline management and

upselling/cross-selling to increase win rates, deal

velocity and average sales price.

■ Do not make purchase decisions solely on model

performance during proofs of concept; also

consider factors such as data sources, integration

options, industry expertise, customer references

and overall customer experience.

Market Definition

This document was revised on 4 October 2016. The

document you are viewing is the corrected version. For

more information, see the Corrections page on

gartner.com.

This market encompasses an emerging category

of effectiveness and productivity applications for

B2B sales and marketing professionals. Software

as a service (SaaS)-based applications are used at

different points of the sales funnel for both prospects

and existing customers. While traditional CRM lead

management and sales force automation (SFA) offer

some functionality to help marketers and salespeople

make more effective decisions and are starting to

incorporate artificial intelligence (AI) and machine

learning, most of the analytics they incorporate

are based on predefined rules and diagnostic and

descriptive analytics.

The solutions in this market leverage a range of predictive and, in some cases, prescriptive analytics techniques and models to enable better decision making based on a combination of internal and external data at both account and contact levels. They also use machine-learning techniques to improve accuracy over time as more data is added to the model.

The market includes two discrete types of solutions. One set of applications is typically used by marketers (or, in some cases, sales development reps [SDRs]) and covers a set of use cases higher up in the sales funnel). The use cases are shown in Figure 1. The models include both fit (propensity to buy) and intent (likelihood that a company is actively looking). A combination of first-party internal data from CRM lead management and SFA systems, along with third-party external data from the web, proprietary and public databases, is used to build the models.

9

Source: Gartner (September 2016)

Figure 1. Predictive B2B Marketing Use Cases

The other set of applications is typically used by those in sales roles, including sales leadership, frontline sales managers, sales reps, SDRs and sales operations leaders. They include a set of use cases for the middle and latter part of the funnel, as well as with existing customers. These use cases are shown in Figure 2. While some external third-party data may feed the model, the models for predictive sales applications rely more heavily on first-party data from SFA systems, as well as emails and calendar appointments. In some cases (particularly for upsell/cross-sell),

data from ERP systems and data warehouses are included in the model.

Many vendors that sell solutions to cover the marketing use cases also provide models for upsell/cross-sell identification. And while demand generation models can provide accounts and contacts for use in CRM lead management systems, SDRs and sales reps can use those same models for prospecting. In addition, Gartner expects to see vendors moving down or up the funnel to cover additional use cases, so both types of solutions are being included in a single Market Guide. Buying processes for these types of solutions are often led by IT

10

Source: Gartner (September 2016)

Figure 2. Predictive Sales Use Cases

or analytics teams, particularly for larger enterprises or those outside of high-tech companies. But since the solutions are all SaaS-based, many of the buying processes for emerging and high-growth technology providers are led by demand generation/marketing operations and/or sales operations, with IT playing an advisory or supporting role.

Predictive applications designed to improve renewal rates and optimize pricing are treated as discrete markets by Gartner and are no longer included in this guide. While SFA vendors have some opportunity scoring capabilities, that is not the primary purpose of the applications, so they are not included. Vendors that offer predictive B2B marketing or sales functionality as part of a service rather as a stand-alone product (including ServiceSource and Revana) or through a data or advertising platform (including Madison Logic) or those that only very recently added predictive capabilities to their platforms (including Avention) are not included. Finally, solutions that use

rule-based approaches (but not data science techniques) are not included.

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Market Direction

The market has grown and matured since the

introduction of the last Market Guide in early 2015,

but it still exhibits the characteristic of a fast-growing,

yet immature, early-stage market. Adoption has

largely occurred from larger or high-growth technology

providers in the U.S. However, both predictive B2B

marketing analytics and predictive sales analytics

have been included in Gartner Hype Cycles for the

last two years. They both have “high” benefit ratings,

“emerging” maturity ratings and market penetration

rates of 5% to 20% of their target audiences.

Predictive B2B marketing analytics is at the Peak of

Inflated Expectations, while predictive sales analytics

is positioned at the beginning of the Trough of

Disillusionment. Given the characterization of the

market, adopters may see significant benefit but also

experience the trial and error and potential need to

switch vendors that come with this type of market.

Gartner estimates that the market will see $100 million to $150 million in vendor revenue by the end of 2016. Despite the market’s comparatively small size, Gartner believes the aggressive positioning in the Hype Cycles is justified because of the high growth potential (both outside the U.S. and in other B2B industries) and the compelling ROI that clients have achieved using these types of solutions. The market has exhibited signs of maturity (especially in the high-tech industry in the U.S.), and vendors in the space have improved their solutions (as well as the customer experience), allowing buyers to move past the pilot or proof-of-concept stage and roll out the applications more broadly. As a result, buzz has increased, and Gartner has seen a noticeable uptick in client inquiries, including from clients in financial services, life sciences, business services and other industries.

Several trends have led to increased demand for solutions. First, account-based marketing (ABM) has emerged as a key investment area for many B2B companies, and predictive analytics can improve both account selection and demand generation elements. Many predictive B2B marketing analytics providers were

quick to position their solutions as key enablers of ABM.

Next, forecasting and pipeline management have become

more challenging for many B2B sales leaders as buyers

exert ever more control over their buying processes.

Many leading SFA tools are the system of record for

forecasting and thus provide only basic forecasting

capabilities. Sales operations teams often have to

spend hours managing forecasts in Excel or business

intelligence (BI) tools on a regular basis. Predictive sales

analytics applications not only provide huge productivity

boosts by automating this largely manual task, but

also provide greater accuracy and visibility around the

expected outcome of individual deals, as well as the

likelihood of meeting forecast targets.

Finally, both lead management and SaaS SFA

applications have reached mainstream adoption, with

the former at 20% to 50% adoption and the latter at

more than 50% adoption in the 2016 Hype Cycle for

CRM sales. Many adopters of predictive analytics have

used one or both of those solutions for three to five (or

more) and have the capacity and desire to take on new

projects. With predictive solutions starting at $25,000

per year, many of the more sophisticated B2B

marketing and sales operations leaders have started

to look at predictive analytics as a potential answer to

some of their more vexing problems.

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All the vendors in this guide are privately held and

are either venture-backed or “bootstrapped” with

capital from their own founders. There has been some

consolidation since the last Market Guide, with smaller

vendors exiting the market. For example, Fliptop was

acquired by LinkedIn, and SalesPredict was purchased

by eBay, in both cases for their machine-learning

capabilities, and InsideSales.com bought C9. However,

new entrants have more than made up for those exits.

Gartner does not expect to see a new market entrant

that solely addresses the current use cases through

2017 (at least in North America) but does expect

more traditional vendors, such as Oracle or Adobe,

to enter through acquisition of one of the existing

predictive B2B marketing vendors. Salesforce and

Microsoft have acquired AI companies and may also

choose to make a purchase or investment to round out

their existing capabilities (although both have equity

investments in at least one vendor in this guide). Most

predictive vendors were able to raise money before

the venture capital downturn in late 2015 and early

2016, and some are also aiming for cash-flow break-

even in the next six to 12 months. Nevertheless, the

market (especially inside the U.S.) is crowded, and one

or more vendors in this guide may find it difficult to

survive as an independent company.

With the need to become profitable and the burden

of acquiring local data, most North America-based

vendors have focused close to home and shied away

from international expansion, at least in terms of

targeting companies outside North America or hiring

salespeople in other regions. (They do support

international sales and marketing teams from North

America-based customers.) A few vendors included in

this guide are based in the U.K. or France, and they

expect to target Germany and other Western European

countries in the next year. No vendors report targeting

Australia, Asia or Latin America in any meaningful way.

For the marketing use cases, data can be an issue in

certain countries, particularly those with double-byte

character sets. (Fuzzy logic matching is the most

problematic in Asia.)

Since the last Market Guide, the predictive models

have moved from being predominantly “black box,”

where the signals that drive the models are hidden,

to being more open and transparent, which Gartner

believes is a positive step. However, IT and sales and

marketing leaders need to be careful to figure out

what signals they want to expose to sales reps within

the account, opportunity and lead objects in the SFA

system. It is crucial to find the right mix between

providing enough information to build trust and

making the data more actionable versus providing too

much and confusing the rep or SDR.

Differentiation remains an issue for most vendors

discussed here. On the predictive B2B marketing side,

most vendors utilize similar data science techniques,

create models that can self-tune, support the same

use cases, source similar third-party data (including

intent data from Bombora and others) and offer

rapid turnaround (or self-service) model creation. The

model creation time used to be a differentiator, but

that has largely been erased. The lack of apparent

differentiation and the typically short contracts (12 to

18 months is common) have made it easy for vendors

to poach customers away at the end of their contracts.

On the predictive sales side, similar differentiation

issues exist, especially because external data is

less important. There are some more clear points

13

of differentiation across both marketing and sales,

and they are called out as part of the sample vendor

write-ups. While model accuracy in a proof of concept

is an important factor, Gartner recommends that IT

application leaders who support sales or marketing

(and anyone in the buying process) also consider a

wide range of factors, including focus, integrations,

customer references and product vision, when making

recommendations or decisions.

Market Analysis

More than 20 vendors offer SaaS-based predictive

analytics applications specifically for use by B2B

sales and marketing professionals. While outbound

SDRs (and some sales reps) get engaged at the top

of the sales funnel in a prospecting capacity (through

emails or phone calls), most predictive use cases at

the top of the funnel are the domain of marketing

professionals, including those in demand generation,

marketing operations and product marketing.

Here is more information about the different types of

predictive B2B marketing Use Cases:

■ Total addressable market (TAM) identification —

B2B companies often want to understand how big

of an opportunity exists before entering a market

or making staffing and investment decisions. While

a TAM number may exist, not all companies in

the market are easily addressable. The predictive

models can identify the size of the market (both in

revenue and number of accounts) for which their

solutions would address and the total roll-up of

all companies in a market with a fit score above a

certain level. Sales operations leaders can also use

this data for territory planning purposes.

■ Vendors: EverString, GrowthIntel, Infer, Mintigo,

MRP, Radius

■ Segmentation — Predictive models can be used

to create segments of accounts based on signals

(fit or intent) rather than traditional firmographics.

These groups of accounts can be the basis for

campaigns in lead management systems or

segment-based ABM programs. As predictive

signals change, the segments change with them.

■ Vendors: 6sense, BrightTarget, Datanyze,

EverString, GrowthIntel, Infer, Lattice Engines,

Leadspace, Mintigo, MRP, Radius, SalesChoice

■ Account selection — One of the fastest-growing

predictive use cases is to identify the best

accounts to select for an ABM program. Marketers

use predictive models to highlight anywhere from

a few dozen to more than a thousand accounts

and tier them based on propensity to buy (fit,

intent or both). The accounts are then exported for

campaign orchestration to lead management, web

personalization and advertising platforms.

■ Vendors: 6sense, BrightTarget, Datanyze,

EverString, Infer, InsideSales.com, Lattice Engines,

Leadspace, MarianaIQ, Mintigo, MRP, Radius,

SalesChoice

■ Demand generation — While some B2B

organizations (particularly those with subscription-

based offerings, free trials and freemium solutions)

are blessed with more inbound leads than they

can effectively manage, most are not. Marketers,

SDRs and sales reps (both generally and as part of

account-based programs) are constantly looking

14

to expand the people to whom they prospect and

have turned to predictive-driven solutions instead

of traditional lists. Vendors offer predictive models

to identify companies based on fit and intent and

then deliver contacts that can be exported to lead

management or SFA systems.

■ Vendors: 6sense, Datanyze, EverString,

GrowthIntel, IKO System, Infer, Lattice Engines,

Leadspace, MarianaIQ, Mintigo, MRP, Radius

■ Lead scoring — Predictive lead scoring was the

initial marketing use case and far away the most

mature one. Traditional lead scoring is based on

two dimensions (demographic/firmographic and

engagement), while predictive lead scoring makes

use of more (and more relevant) signals that are

correlated with propensity to buy to go along with

engagement and/or intent. These models have

generally proven to be far more accurate than

traditional lead scoring at predicting the likelihood

of a lead converting into an opportunity and

closing.

■ Vendors: 6sense, BRIDGEi2i, BrightTarget,

Datanyze, DxContinuum, EverString, Infer,

InsideSales.com, Lattice Engines, Leadspace,

Mintigo, MRP, Radius

Fewer vendors offer solutions to address sales

rather than marketing use cases, although many of

the marketing vendors do provide upsell/cross-sell

models, which share similarities with other solutions

they offer. But while the marketing models rely heavily

on external data, the sales models are more reliant on

internal data.

■ Forecasting — As the system of record, traditional

SFA tools often lack the forecasting and pipeline

management capabilities required by sales

operations leaders, while the data going into

the forecasts (typically entered by the sales rep)

often lacks the rigor and accuracy that sales

organizations require. Predictive forecasting

models solve both problems by automating

the forecasting and pipeline management

processes and using data science models to score

opportunities and roll them up at various levels.

Sales leaders and managers can see the forecast

revenue at product, team or geographic levels,

while reps can gain better insight for their own

opportunities and quota attainment.

■ Vendors: Aviso, BRIDGEi2i, BrightTarget, Clari,

DxContinuum, InsideSales.com, SalesChoice,

TopOPPS

■ Opportunity scoring — Predictive forecasting has

replaced the need for stand-alone opportunity

scoring for many B2B companies, but there are

still situations where opportunity scoring can be

helpful for both sales reps and their managers.

Understanding the true likelihood of close (and the

close date) instead of going off what the rep has

entered alone can help dictate focus and attention.

Vendors are also moving toward giving prescriptive

guidance and coaching (also true with the

forecasting solutions) to help reps and managers

understand how to improve the likelihood of

closing a deal.

■ Vendors: BRIDGEi2i, Clari, DxContinuum, Infer,

InsideSales.com, SalesChoice, TopOPPS

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■ Upsell/cross-sell — Many larger and more

established companies add far more revenue from

growing existing accounts versus signing new

ones, so predictive upsell and cross-sell models

have been around for longer than most other use

cases. The applications primarily rely on internal

data, but not all of it is in CRM systems. Models

that incorporate transactional data from order

management systems and data warehouses

are usually more accurate. Some vendors have

extraction, transformation and loading (ETL) tools

or various data layers to get at that data, while

others need the customer to provide extracts. The

models provide not only the accounts to target, but

also the solutions to offer. Some B2B companies

build their own systems at first, but the care and

feeding of the system often drive a switch to a

third-party solution.

■ Vendors: 6sense, BRIDGEi2i, BrightTarget,

DxContinuum, Entytle, EverString, Infer, Lattice

Engines, Mintigo, Radius, SalesChoice

Representative Vendors

The vendors listed in this Market Guide do not imply an

exhaustive list. This section is intended to provide more

understanding of the market and its offerings.

Note: The “high-tech industry” includes technology

vendors (hardware and software), service providers and

communications service providers (CSPs).

6sense

www.6sense.com

Use Cases: Segmentation, account selection, demand

generation, lead scoring, upsell/cross-sell

San Francisco-based 6sense was created in 2013 and

has raised $46 million in venture funding. It targets

marketers and supply sales leaders with outbound

prospecting tools that identify when buyers are in-

market, helping them answer the answer of “timing.”

It also offers lead scoring and upsell/cross-sell

predictions. While its customers certainly leverage

6sense’s account and contact/lead fit-based models,

6sense has invested more heavily in time-based, intent

modeling techniques than any other vendor in this

guide. 6sense’s patented methodology predicts when

prospects are in an active buying cycle and where

the prospect is in his or her buying journey. It has

built a private data network that includes publishers,

search engines, blogs, community forums and many

other sources (and augments data from other intent

providers). 6sense also utilizes IP to company matching

and cookie syncing and incorporates time-based and

relativity-based predictions into its models to gauge

intent before someone fills out a form or “raises a

hand.” 6sense leverages its publisher relationships

to allow marketers to reach their buyers through

their ABM efforts. The time-based intent modeling

capabilities feature prominently into the company’s

positioning and messaging and customer testimonials.

6sense recently introduced a lower entry price to appeal

to midmarket companies, but it has historically targeted

large enterprises in high-tech and other verticals. The

lower entry pricing and shorter-term contracts now

16

make it easier for customers to test and buy 6sense’s

solutions. 6sense is often a good fit for companies that

are in highly competitive markets, where understanding

buyer readiness (timing) is critical. 6sense was named a

Gartner Cool Vendor in 2015.

Industries Represented: Financial services, high tech,

manufacturing, medical devices, professional services

Supported Integrations: Bombora, Forbes (and other

large publishers), Integrate, Madison Logic, Marketo,

Oracle (BlueKai and Eloqua), Salesforce

Notable Customers: BlueJeans Network, Dell,

GE, NetSuite

Pricing: Starts at $50,000 per year for midmarket

companies (higher for larger companies) and

increases based on the number of products that are

being modeled

Aviso

www.aviso.com

Use Cases: Forecasting

Menlo Park, California-based Aviso has been in business

since 2012 but released its first predictive forecasting

solution in early 2015. It has 65 employees and has

raised $23 million to support its efforts. The company

creates an integrated forecasting view across all

revenue sources that is completely consistent from

the global level down to the individual BU, region or

rep. This is built on top of a forecasting engine that

utilizes predictive and prescriptive analytics models

to determine the likelihood of a deal closing, the

date it will close and how much it will be worth. Aviso

provides capabilities to help sales operations and

sales leadership get early warnings and easily see the

discrepancies between traditional and Aviso forecasts

and dynamically highlights the recent changes that have

impacted its models. Aviso’s architecture has the ability

to incorporate multiple data sources, including CRM,

email and calendar data, in its models. One of the

capabilities it believes to be unique is modeling around

billings, revenue and pipeline, instead of just bookings

data (to better predict the actual size of the deal).

Aviso also provides automated alerts when its models

indicate changes, such as a deal being likely to slip.

Aviso focuses on companies with more than 50 sales

reps. While it supports other SFA systems, a large

fraction of the company’s clients use Salesforce.

Aviso has been in this market for less than two years

but already has more than 40 customers. It is a good

fit for midmarket and enterprise customers across

several industries. Aviso is able to create forecast

models against multiple SFA systems simultaneously,

which can allow customers not to have to rush to

integrate SFA systems after making an acquisition.

Industries Represented: High tech, manufacturing,

media, professional services

Supported Integrations: Gmail, Microsoft Dynamics,

Microsoft Exchange, NetSuite, Oracle, Salesforce, SAP

Notable Customers: Hewlett Packard Enterprise,

Marketo, Nutanix, Splunk

Pricing: Aviso has several editions of the product, but

it starts at $900 per user per year, with a $20,000-per-

year minimum spend.

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BRIDGEi2i

www.bridgei2i.com

Use Cases: Lead scoring, opportunity scoring,

forecasting, upsell/cross-sell

Bangalore, India-based BRIDGEi2i is the only

vendor in this guide headquartered outside North

America, the U.K. or France. The company started

as a predictive analytics consulting firm in 2011

and rolled out its first product in 2014. Most of its

customers are in the U.S., but it also has some in

Asia (in India, in particular). BRIDGEi2i offers stand-

alone opportunity scoring, as well as a predictive

forecasting solution. The solutions leverage Monte

Carlo simulations, and both managers and reps can

do scenario modeling and what-if planning in their

native Force.com application or through a third-party

visualization tool. Reps can also benchmark their

expected performance against those of peers. For

sales leaders and sales operations leaders, BRIDGEi2i

can offer recommendations to help them act on the

data. The company also offers a stand-alone upsell/

cross-sell model and a lead scoring solution, based on

fit and intent (although it leverages fewer external data

sources than most other solutions in this guide).

BRIDGEi2i targets large companies in a variety of B2B

industries (it has 10 customers in the Fortune 100

alone) with solutions that are more custom-designed

rather than off the shelf. It most commonly replaces

homegrown solutions. Many of its customers have

internal data scientists; BRIDGEi2i’s professional

services team works closely with them around

potential solutions. Although some of its solutions

are immature and lacking in functionality when

compared with competitors’, the professional services

capabilities can often fill in the gaps. BRIDGEi2i’s

flexibility and common data model make it a fit for

very large companies that want an alternative to more

packaged options from other vendors.

Industries Represented: Consumer packaged goods

(CPG), financial services, high tech (manufacturing),

insurance, retail

Supported Integrations: Marketo, Salesforce

Notable Customers: Not indicated.

Pricing: Starts at $50,000 per year and increases

based on customizations, number of products, scope,

models and sales team size

BrightTarget

www.brighttarget.com

Use Cases: Segmentation, account selection, lead

scoring, forecasting, upsell/cross-sell

West Midlands, U.K.-based BrightTarget started as an

innovation department within a BI/data consultancy

in 2012 and was established as a separate business

in 2014. It was known as Kairos until late last year,

when it sold off the consulting firm. While BrightTarget

sells to high-tech companies in the U.K., it focuses

on industries that others have paid less attention to,

including building services and media. The dataset it

builds also reflects these priorities as the BrightTarget

Business Index includes data on small building

companies. BrightTarget also differentiates by taking

a customer lifetime value (CLTV)-driven approach to

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its modeling, showing those values even when scoring

leads. As the market has matured, BrightTarget

has done the same thing by significantly reducing

the time to develop models and rolling out a new

lightweight forecasting tool. BrightTarget also offers

marketing attribution capabilities and will roll out TAM

identification and demand generation solutions in the

coming months.

BrightTarget remains focused on the U.K. market and

the current industries for which it has had success.

Upsell/cross-sell and account scoring (from purchased

lists) remain its entry points. The CLTV-related

capabilities address a common pain point for Gartner

clients as they look to identify the best accounts to

target for expansion. As BrightTarget rolls out more

top-of-the-funnel solutions, it will have an opportunity

to scale its business but will face greater competition,

as well.

Industries Represented: Building services, high tech,

media

Supported Integrations: Adobe Marketing Cloud,

Force24, Marketo, Oracle (Eloqua), Salesforce (Pardot

and Sales Cloud)

Notable Customers: BSS Industrial, Company Check,

Euromoney Institutional Investors, Speedy Hire

Pricing: Starts at $32,000 per year and increases

based on the number of models created and the

amount of data being used

Clari

www.clari.com

Use Cases: Forecasting, opportunity scoring

Sunnyvale, California-based Clari has been in existence

since 2013 (with a product in 2014) but traces its

predictive analytics legacy back to 2005. The company’s

founders (and much of its staff) came from machine

pioneer Clearwell, which was sold to Symantec in

2011. Clari has raised $46 million to date and delivers

predictive models for pipeline inspection, deal and

forecast management — what Clari customers call the

“opportunity to close” process. It has a wide range of

prepackaged integrations (although it supports only

Salesforce among SFA vendors). Clari was the first to

bring email and calendar data into forecast and deal

models. The company can track forecast and deal detail

changes in real time without exporting, and it features a

“graph” that prioritizes sales reps’ tasks across key deals

to drive better productivity and a “grid” that helps both

reps and managers with real-time updated deal progress.

Clari recently announced its AI-driven messaging

platform that proactively prescribes actions to sales

teams (called “nudges”) to drive behavior and actions

that increase deal velocity and close probabilities.

With a higher starting price than some of its

competitors, Clari focuses on pre-IPO, private-equity-

backed and public companies, mainly in technology,

media and professional services. Clari is a good fit for

upper-midmarket companies and enterprises that are

looking at applying predictive analytics to drive better

pipeline management and more accurate forecasting

and use Salesforce for SFA.

19

Industries Represented: High tech, media,

professional services

Supported Integrations: Box, Dropbox, Evernote,

Gmail, LinkedIn, Microsoft Exchange, Salesforce

Notable Customers: Hewlett Packard Enterprise, Intel,

Juniper Networks, Palo Alto Networks

Pricing: Clari declined to provide pricing details for

this guide. Please contact the vendor directly for

pricing details.

Datanyze

www.datanyze.com

Use Cases: Segmentation, account selection, demand

generation, lead scoring

Four-year-old San Mateo, California-based Datanyze has been in the predictive space for only a year, but it has built up a strong following in other areas (more than 30,000 users) by tracking technology and mobile installs and alerting SDRs when an account has installed a particular product. (Datanyze also provides a free browser plug-in.) Datanyze evolved from a technographic information provider into a full-fledged data platform (with 45 million domains and contact information) and then added predictive models for both marketers and SDRs to better take advantage of this data. They can also use the data platform to enrich account, lead or contact information. The company currently leverages only fit models, but it has its own IP tracking capabilities so that anonymous website traffic can be easily added to them. Intent is also derived by looking at whether an account recently

added a competing or complementary product.

The predictive capabilities are packaged as an add-on

to the data platform, but the total solution price (data

and models) is still lower than most, if not all, other

predictive solutions. The company focuses exclusively

on selling to technology companies (especially

SaaS providers). While Datanyze gets a foothold in a

company through the technology tracking capabilities

being used by SDRs, the data platform solutions are

also purchased by marketers to help with demand

generation. The predictive add-on is increasingly being

purchased, especially for top-of-the funnel processes.

Datanyze has raised only $2 million in venture funding

(in a 2014 seed round). The company is a fit for

emerging SaaS companies looking for cost-effective

predictive demand generation solutions. Datanyze was

named a Gartner Cool Vendor in 2016.

Industries Represented: High tech (primarily SaaS)

Supported Integrations: HubSpot, Marketo, Salesforce

Notable Customers: HubSpot, Marketo, Namely,

New Relic

Pricing: The data management solution with predictive

add-on starts at $20,000 per year and increases as a

result of customizations and additional model creation.

DxContinuum

www.dxcontinuum.com

Use Cases: Lead scoring, opportunity scoring,

forecasting, upsell/cross-sell

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Fremont, California-based DxContinuum is one of

the newer players in the space (with a product since

2014 and funded in 2015) and takes one of the

more unique approaches to its targeting and product

portfolio strategies. The company mainly targets sales

operations and sales leadership with an upsell/cross-

sell solution then adds on an opportunity scoring

solution that rolls up into a predictive forecast. It

also offers a lead scoring solution. DxContinuum

typically deals with more complex sales processes,

and it offers a data preparation layer to transform

data to more easily be used in a model. The models

it creates are highly sensitive to changes in data and

how often that data changes. DxContinuum has several

unique capabilities, including the creation of a family

of models with different regression techniques for a

single use case (selecting the one that produces the

best fit) and even the ability to allow a customer to

run the solution on-premises. DxContinuum is one of

a few vendors in this guide with a Salesforce security

certification and will be introducing a Salesforce Wave

Analytics capability later in 2016.

As one of the smaller and least capitalized vendors in

the market (it closed $4 million in a Series A round in

3Q15), the company is mindful about staying focused

on a “sweet spot.” While DxContinuum will pursue

midmarket deals opportunistically (companies with

at least 50 reps), it has typically sold to much larger

technology companies, with a complex product mix

that uses Salesforce. The security, data transformation

and deployment options are far more important for

larger companies. DxContinuum is a good fit on the

sales operations side for larger high-tech companies

(including those with a large indirect channel),

especially around upsell/cross-sell use cases.

Industries Represented: High tech

Supported Integrations: Marketo, Oracle (Eloqua and

Sales Cloud), Salesforce

Notable Customers: Adobe, Akamai, Cisco, VMware

Pricing: $30,000 per year for 25 users; additional

costs for additional users

Entytle

www.entytle.com

Use Cases: Upsell/cross-sell

Mountain View, CA-based Entytle is one of the newer

players in the space (with a product since 2014 and a

product shipped in 2015). It focuses on an area of the

market that has largely been ignored by other vendors.

Entytle sells aftermarket “entitlement automation”

solutions to industrial manufacturers. The solutions

utilize predictive models to identify low-wallet-share

customers and then recommend upsell and cross-sell

opportunities for spare parts, consumable items and

service contracts. Data from a range of applications

(including ERP, contact center, service and support)

feeds the model, either through real-time integration

or lightweight extracts. Since many machines and

other industrial products through an indirect channel

don’t currently “phone home,” Entytle’s solutions

help manufacturers infer usage and behavior largely

based on the “traces” they leave on different systems

and through network data of similar devices and

manufacturers. Data is presented for individual

solutions and bundles at both an account and

opportunity level. Entytle has raised $8 million in seed

21

funding and will release a campaign planner tool and a

contract upsell capability later in 2016.

Entytle typically targets discrete manufacturers

with more than $200 million in revenue that want

to upsell spare parts or service contracts, replace

consumables, manage their field workforce, or gain

better visibility into the state of their installed base.

While other vendors offer predictive solutions for

upselling and may compete with Entytle in deals for

high-tech manufacturers, Entytle is often unopposed

when selling to industrial equipment manufacturers.

The aftermarket entitlement-specific use case comes

up only in a handful of predictive-analytics-related

client inquiries today, but with more than $1 trillion

spent annually on industrial aftermarket purchases,

Gartner expects to see increased interest in solving

this problem moving forward.

Industries Represented: High tech (hardware and

manufacturing), industrial manufacturing

Supported Integrations: Marketo, Microsoft Dynamics,

Salesforce (Pardot, Sales Cloud and Service Cloud),

SAP, ServiceMax

Notable Customers: Hayward Gordon, Johnson

Controls, Philips Healthcare, Teledyne

Pricing: Starts at $100,000 per year (plus a one-time

set up fee of $50,000) and increases based on the

amount of pipeline increased from

Entytle-based recommendations

EverString

www.everstring.com

Use Cases: TAM identification, segmentation, account

selection, demand generation, lead scoring, upsell/

cross-sell

San Mateo, California-based EverString started in

2014 and quickly secured more than 100 customers

and almost $79 million in venture capital funding. It

supports the full gamut of predictive B2B marketing

use cases (including ones not in the guide, such as

ad targeting) and some sales-related use cases, as

well. EverString claims to take a different approach

with its modeling than other vendors, preferring

to look at a wider range of signals to identify the

company’s “DNA” rather than traditional fit and intent

models. Its approach to creating data revolves around

extracting machine-learning insights, in addition to

crawling and ingesting data from both proprietary and

commercial sources. The company also has separate

algorithms for segmentation and scoring. EverString

targets marketers, sales development managers and

sales leaders and provides self-service capabilities

for individual marketers, such as expansion audience

building and segmentation on the fly. It helps sales

reps and SDRs identify accounts and contacts both

inside and outside their CRM system that are similar

to the ones they just closed. EverString was one of

the first predictive analytics vendors to promote its

solutions as a more data-driven way to do prospecting

and account selection for ABM.

EverString has used its venture capital investment to

aggressively fund its product development and go-to-

market efforts. This has allowed the company to go

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after a wide range of industries and simultaneously

serve the midmarket and enterprise segments.

EverString has a low entry price that is attractive to

smaller companies, but it can scale its offerings (and

the associated price) to meet the needs of much larger

companies. Given its broad portfolio capabilities,

EverString can be a fit for many North American

companies looking at predictive B2B marketing or

sales solutions. EverString was named a Gartner Cool

Vendor in 2016.

Industries Represented: Financial services,

healthcare, high tech, professional services

Supported Integrations: Marketo, Microsoft Dynamics,

Oracle (Eloqua), Salesforce

Notable Customers: Apttus, Comcast Business,

IBM, Salesforce

Pricing: Starts at $14,000 per year for the entry-level

EverString Audience Platform and increases with

database volume and additional use cases. Contacts

for demand generation are priced separately.

GrowthIntel

www.growthintel.com

Use Cases: TAM identification, segmentation, demand

generation, upsell/cross-sell

London-based GrowthIntel was founded in 2011

and has built its business selling top-of-the-funnel

predictive solutions to U.K.-based companies that

target small businesses. GrowthIntel’s solutions are

mostly used by chief marketing officers (CMOs)

and demand generation leaders to build segments

for outbound campaigns to net-new prospects, but

it also offers models for prioritizing inbound leads

and targeting existing customers. Unlike most other

vendors in the guide, GrowthIntel collects primary

data instead of relying on third-party data. This

encompasses more than 4 million small businesses

in the U.K. and is augmented with credit reporting

data from third parties and internal data to build the

models (it currently has only direct integrations with

Salesforce but can export to CRM lead management

tools). Although each client’s data is always kept

confidential, GrowthIntel also makes use of a network

effect across the interactions that its customers have

with small businesses, which provides an additional

level of prediction beyond fit.

With only a few other vendors targeting the U.K.

market and none of them really focusing on the

same type of use cases/industries (segmentation

and demand generation for companies targeting

small businesses), GrowthIntel has had the market

largely to itself. In client inquiries with Gartner, this

company ends up being mentioned in conjunction

with traditional data vendors rather than with other

predictive marketing vendors, but GrowthIntel is a

fit for U.K.-based companies that are targeting small

businesses. The company plans to expand to France

and Germany in the near future, which will broaden

its market opportunity, assuming it can collect the

primary data it needs, but it may also see greater

competition in its home market from vendors in France

and the U.S.

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Industries Represented: Financial services,

high tech, logistics

Supported Integrations: Salesforce

Notable Customers: BT, Pure360, PwC, Zurich Reinsurance

Pricing: Starts at £50,000 per year. Pricing increases are

based on the number of opportunities that were created.

IKO System

www.iko-system.com

Use Case: Demand generation

Paris-based IKO System has been in the predictive

demand generation market since 2012. It targets

Western European B2B companies (mainly in France,

but also in the U.K., the Netherlands and Germany)

that are looking to generate more leads from net-new

customers. Despite raising only €3 million in venture

funding, it has acquired more than 200 customers over

the last four years. IKO System scores accounts with

an approach toward understanding an account’s “DNA”

and scores the contacts it provides, as well. It will be

adding a lead scoring capability for inbound leads later

in 2016, but the company has also developed an inside

sales solution to augment its portfolio.

Most other vendors in the market have ignored France

and the rest of Western Europe (outside the U.K.),

and IKO System has capitalized on that void to sell

predictive demand generation solutions. Given the

breadth and quality of the Europe-centric data it can

utilize, IKO System is a fit for both local companies

and regional arms of U.S. companies that are looking

for better demand generation options, especially in

France. The inside sales solution may prove to be a

useful addition to the IKO System portfolio because

it is the only vendor in Europe that can currently

provide the combination of predictive scoring, net-new

contacts, prescriptive guidance around channels and

interactions to sales reps and SDRs, and automation

to make that process easier. IKO System was named a

Gartner Cool Vendor in 2015.

Industries Represented: Financial services, high tech,

insurance, professional services

Supported Integrations: Gmail, HubSpot, Marketo,

Outlook, Salesforce

Notable Customers: Infor, Talend, TIBCO

Software, Tidemark

Pricing: Starts at €12,000 per year and increases

based on volume. The inside sales solution is priced

separately.

Infer

www.infer.com

Use Cases: TAM identification, segmentation,

account selection, demand generation, lead scoring,

opportunity scoring, upsell/cross-sell

Mountain View, California-based Infer launched in

2010 and has signed up more than 140 customers,

the vast majority of them being high-growth SaaS

companies. The company has raised $35 million in

venture funding to support its efforts. Infer provides

separate fit and behavior models (with intent part of it).

24

It also layers on a profile management capability (with

access to thousands of data points) for segmentation

and account selection, and the profiles can be pushed

into Salesforce and Marketo, enabling marketers to go

beyond simple smart list creation in the latter solution.

Infer has been actively promoting the value of its

solution for ABM efforts and has formed partnerships

with Terminus and AdRoll on the activation side

(integration is done via Salesforce). The company also

supports unique cases, such as scoring any external list

before marketers have to purchase them. Infer is one of

only a few predictive B2B marketing vendors to provide

models for opportunity scoring.

With a large volume of SaaS companies as customers,

Infer is a fit for midmarket and upper-midmarket SaaS

companies, especially for account selection and lead

scoring projects. However, newer entrants to the space

have aggressively gone after Infer customers when

their initial agreements are set to expire, causing the

company to focus on defending its installed base. As

Infer has expanded its solution set, the company has

the opportunity to expand within its own customer

base, but Gartner still expects to see Infer branch into

other markets in 2017 and beyond. Infer was named a

Gartner Cool Vendor in 2015.

Industries Represented: High tech (primarily SaaS)

Supported Integrations: Google Analytics, HubSpot

(fit score only), Marketo, Oracle (Eloqua), Salesforce

(Pardot and Sales Cloud)

Notable Customers: HubSpot, New Relic,

Tableau, Zendesk

Pricing: Starts at $30,000 per year and increases

based on the number of models. Net-new contacts (for

demand generation solution) are priced separately.

InsideSales.com

www.insidesales.com

Use Cases: Account selection, lead scoring,

opportunity scoring, forecasting

Provo, Utah-based InsideSales.com is a unique player in the predictive B2B marketing and sales application market. Long known as a pioneer in the inside sales solution market with a platform that includes a dialer, email templates and gamification capabilities, the company has also had some innovative predictive analytics capabilities with its Neuralytics engine and NeuralView product. Using network data of more than 150 million customer profiles (based on more than 100 billion interactions across its more than 3,000 customers), the company’s NeuralSort capability can help SDRs predict who to contact, when to contact them, what to say and what channel to use. It can also score accounts, leads and opportunities, providing a NeuralScore based on propensity to close. In early 2015, InsideSales.com acquired C9 (a Gartner Cool Vendor in 2015) and added its predictive forecasting capabilities (now branded as HD Forecast) to the portfolio. The combined platform provides both predictive analysis and prescriptive recommendations, with the latter being among the most extensive of any vendor. InsideSales.com has raised nearly $200 million in venture funding, including investments from

Microsoft and Salesforce.

InsideSales.com has made a shift from largely

targeting small or midsize businesses to moving

upmarket, with more than half of new sales coming

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from enterprises. It is one of the major players in

the inside sales solution market, and NeuralView is

an integral component (and differentiator) of that

platform. All the predictive capabilities are included in

the highest pricing tier, which means that NeuralView

users can add predictive account and lead scoring

for free. InsideSales.com does not provide leads or

contacts for demand generation, so it rarely competes

with other predictive B2B marketing vendors, but its

predictive marketing solutions are a fit for a range of

companies, especially those already using InsideSales.

com’s inside sales solution. The HD Forecast solution

is a fit for enterprises running Salesforce or Microsoft

that are looking to improve forecast accuracy and

pipeline visibility.

Industries Represented: Business services, financial

services, high tech, insurance, professional services

Supported Integrations: Microsoft Dynamics, Salesforce

Notable Customers: ADP, GE, Google,

Thomson Reuters

Pricing: NeuralView is part of the Accelerate edition

subscription, which is $3,540 per user per year. HD

Forecast Professional Edition costs $840 per user

per year for opportunity scoring, while HD Forecast

Enterprise Edition costs $1,080 per user per year and

includes predictive forecasting.

Lattice Engines

www.lattice-engines.com

Use Cases: Segmentation, account selection, demand

generation, lead scoring, upsell/cross-sell

San Mateo, California-based Lattice Engines began

offering predictive solutions in 2006 productized its

initial offering (an upsell/cross-sell solution targeted at

sales) in 2011. Lattice has more than 150 customers

across both marketing and sales use cases (many of

them are larger companies), has raised $75 million

in venture capital, and by Gartner’s estimation, is the

largest (by revenue) pure-play vendor in this guide.

While Lattice’s more deliberate approach won deals

with larger and more security-minded companies (both

in high-tech and other verticals), it was not suitable

for smaller companies. But in 2015 and early 2016,

the company rearchitected its solutions to allow easier

data integration and real-time performance; created

a new user interface (with all models visible from a

single portal); beefed up integrations with Salesforce,

Oracle (Eloqua) and Marketo (to aid with ABM and

other campaigns); expanded its data platform to

include international, contact and intent data; and

added self-service modeling capabilities. It also added

account selection and demand generation solutions

(which includes data diagnostics, such as cleansing

and enrichment tools).

Despite the rapid growth of several other vendors, and

an internal focus in 2015 on rearchitecting its solutions,

Lattice remains the most visible “face” of the market.

With its focus on security, level of integrations and ETL

tools, the company is a fit for enterprise clients (both

in high-tech and other industries) and/or companies

planning to deploy in multiple regions. Gartner clients

report that the company’s go-to-market approach is

unique in the way it addresses complex problems and

help customers operationalize the insights from the

models. Lattice is one of the few vendors that can

26

recommend key plays at both the lead and account

level across the entire funnel. Lattice was named a

Gartner Cool Vendor in 2013.

Industries Represented: Business services,

distribution, financial services, high tech, industrial

manufacturing

Supported Integrations: Marketo, Microsoft Dynamics,

Oracle (Eloqua), Salesforce (Pardot and Sales Cloud)

Notable Customers: Amazon, Dell, PayPal,

SunTrust Bank

Pricing: Starts at $50,000 per year and includes

unlimited models. The price increases based on the

number of contacts and sales users.

Leadspace

www.leadspace.com

Use Cases: Segmentation, account selection, demand

generation, lead scoring

San Francisco-based Leadspace was started in 2010

to provide data for demand generation professionals.

It built up a strong customer base around the quality

of its data. In 2013, it added statistical and machine-

learning models to help clients identify the accounts

and contacts most likely to buy, both in terms of

net-new companies and existing leads. Today, the vast

majority of Leadspace’s more than 120 customers

are using predictive models as opposed to simply

accessing data. However, data remains at the heart

of the Leadspace offering, and the company uses its

Virtual Data Management Platform as a differentiator.

The platform can easily bring in almost any kind of

structured and unstructured data to help clients better

understand the accounts and individual contacts.

Leadspace’s approach is to take data, enrich it and

then blend it with semantic knowledge to get around

data accuracy issues that marketers often encounter.

The company believes it not only makes campaigns

and outreach more effective, but also allows for better

segmentation at the persona level.

For most of its existence, because of its reputation

for comprehensive and accurate data, Leadspace has

competed against traditional data providers instead

of other predictive analytics vendors. (It would often

expand its footprint once it sold data.) But over the last

year and after the company’s last venture capital round

(bringing it to a total of $35 million raised), Leadspace

has become more aggressive in its go-to-market

approaches. The company tends to be a stronger fit

for high-tech and professional services companies that

have large house databases but lack confidence in the

accuracy and the quality of their data. Leadspace was

named a Gartner Cool Vendor in 2016.

Industries Represented: High tech, professional services

Supported Integrations: HubSpot, Marketo, Oracle

(BlueKai and Eloqua), Salesforce (Pardot and

Sales Cloud)

Notable Customers: Autodesk, Microsoft,

Oracle, RingCentral

Pricing: Based on a combination of platform

27

functionality and data volume. Starts at $26,000 per

year, which includes enrichment and predictive scoring

for up to 50,000 records. Net-new predictive discovery

of accounts and contacts is available at additional cost.

MarianaIQ

www.marianaiq.com

Use Cases: Account selection, demand generation

Palo Alto, California-based MarianaIQ is the newest

vendor in this guide. It was founded in 2014 but didn’t

release a product or get a seed round ($2 million) until

2016. The company also focuses on a unique segment

on the market. Its primary capability is to help

marketers reach target personas at named accounts

through multiple channels, starting with Twitter and

Facebook. While some of its customers only use

Mariana’s machine-learning capabilities to match

social media profiles, others use the applications for

account selection (based on fit and intent) for ABM

programs. If you know the names of the people you

want to target, MarianaIQ can identify Twitter handles

and Facebook profiles based on first name, last name

and email collected from a Salesforce database, but

it can also match using fuzzy logic. Clients can also

simply go with the profiles MarianaIQ recommends

based on persona, segment or industry.

Compared with most other vendors in this guide,

MarianaIQ covers a very narrow niche. But with the

demand for ABM programs and the importance of

Facebook and Twitter as advertising channels, there is

potential for high demand for what it offers. Gartner

has seen the most interest for this type of solution

from high-tech companies running ABM programs that

have a more complex sales process and many buyers

to target. MarianaIQ has a deep integration (both

from an orchestration and reporting perspective) with

Marketo and integrates with Salesforce (both Pardot

and Sales Cloud) and HubSpot. It doesn’t currently

integrate with Oracle Eloqua, making it a better fit

for Salesforce customers running other CRM lead

management systems. The company has developed

some additional capabilities to help with lead

nurturing and predict the right call to action, which

may increase its appeal.

Industries Represented: High tech (primarily SaaS)

Supported Integrations: HubSpot, Marketo, Salesforce

(Pardot and Sales Cloud), Twitter

Notable Customers: Finsync, MemSQL,

WhiteHat Security

Pricing: Starts at $30,000 per year (without data)

and $70,000 per year (with data). There are additional

charges for persona creation and data volumes above

10,000 contacts.

Mintigo

www.mintigo.com

Use Cases: TAM identification, segmentation, account

selection, demand generation, lead scoring,

upsell/cross-sell

Founded in 2009, San Mateo, California-based

Mintigo started out providing data for B2B demand

generation professionals and added predictive models

in 2012 based on propensity to buy. By 2013, it had

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expanded its solutions to offer predictive lead scoring,

opportunity scoring, account selection and upsell/

cross-sell solutions. Mintigo’s initial focus has allowed

it to be much less reliant on external data providers

than most other vendors, and Mintigo does its own

intent validation, as well. The company can create

custom attributes for clients, something it believes

provides a competitive advantage. It includes real-time

data enrichment to all lead, contact or account records

in both CRM lead management and SFA systems,

where the records are scored against a predictive

model. It allays security concerns by not storing

contacts in its system. For upsell/cross-sell models,

Mintigo can score multiproduct solutions and bundles.

Mintigo recently added prescriptive sales coaching

capabilities at the lead and account levels and is in

beta with a predictive campaign tool that automatically

personalizes the message and content by persona.

Mintigo focuses on the upper end of the midmarket and

on enterprise accounts in a range of B2B industries. It

has raised $34 million in venture capital funding and

was one of the fastest-growing vendors in the market

in 2015. Mintigo has fewer customers than some other

predictive B2B marketing vendors that it competes with,

but it has been very successful in getting a foothold

within large enterprises and expanding solutions down

the funnel and to support upsell/cross-sell use cases

for large customers. Mintigo’s integrations, security

capabilities and tools make it a good fit for companies

considering more sophisticated ABM programs or when

they have more ambitious or comprehensive strategies

for predictive analytics.

Industries Represented: Financial services,

high tech, media

Supported Integrations: Adobe Campaign, Integrate,

Madison Logic, Marketo, Microsoft Dynamics, Oracle

(Eloqua and Sales Cloud), Salesforce

Notable Customers: Getty, Oracle, Red Hat, SolarWinds

Pricing: Starts at $60,000 per year, with an additional

charge for contacts for demand generation. The sales

coaching solution is priced separately.

MRP

www.mrpfd.com

Use Cases: TAM identification, segmentation, account

selection, demand generation, lead scoring

MRP is a wholly owned subsidiary of Northern

Ireland-based First Derivatives. MRP had partnered

with Framingham, Massachusetts-based predictive

analytics software provider Prelytix to power its

Delta Marketing Cloud managed services for ABM.

MRP acquired Prelytix in early 2015 and renamed

the solution Delta Prelytix. MRP has more than 350

customers across three continents leveraging its

predictive analytics software, with many (but not all)

using it to support the use cases that are included

in this guide. MRP’s models are run off the powerful

Kx database (another First Derivatives portfolio

company). The models are largely intent-based rather

than fit-based. The intent signals help marketers and

SDRs not only understand propensity to buy, but also

better understand where prospects are in their buying

journey to tailor the content and outreach accordingly.

MRP has one of the largest customer bases and a

greater geographic coverage than other providers

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included in this guide. But because MRP also provides

full-funnel managed services, many buyers don’t see

it as a direct competitor to most other predictive B2B

marketing vendors. MRP is a fit for companies that

are looking to power ABM efforts into markets that are

well-established or more transactional in nature and

where intent is the primary criteria for sellers.

Industries Represented: Financial services, high tech

Supported Integrations: Marketo, Microsoft Dynamics,

Oracle (Eloqua), Salesforce (Pardot and Sales Cloud)

Notable Customers: Cisco, CSC, Hewlett Packard

Enterprise, NetSuite

Pricing: Starts at $60,000 per year and scales

based on the countries covered, number of solution

topics and the level of customization to the scoring

algorithm, which can be modified and adapted to

specific client needs.

Radius

www.radius.com

Use Cases: TAM identification, segmentation, account

selection, demand generation, lead scoring, upsell/

cross-sell

San Francisco-based Radius was founded in 2009

and launched its first predictive B2B marketing

application in 2014. It has raised more than $128

million in venture funding, more than all but one

vendor in this guide. Initially, the company was heavily

focused around helping companies across a range

of industries better identify and target the small

businesses that had the highest propensity to quickly

purchase its solution. But over the last year, Radius

has added capabilities to address a wider range of

use cases (especially down-funnel) and to address the

needs of companies selling to enterprises. The Radius

Business Graph of 20 million businesses includes

account and contact data gathered from internal and

external sources and is enhanced by the network

data across interactions of all its customers. It more

recently expanded to include attributes that are

more important for companies targeting enterprises.

Radius has strong integrations with Salesforce and

Marketo, as well as with Facebook, which allows users

to activate custom audience campaigns to the target

account lists and segments that Radius recommends.

The company also promotes rapid self-service model

creation to allow marketers to quickly size and engage

with new segments.

Despite having more than 100 customers (with several

in the Fortune 50) and being really well-funded,

Radius has been more under the radar than some

other vendors in the guide. It has been able to sell

to a broader range of clients (half its customers are

outside high tech), and it would often go unopposed

with companies that sold to small businesses. But

the company’s visibility has increased over the last

year, especially around ABM-related use cases, such

as account selection, demand generation and upsell/

cross-sell. Radius doesn’t charge customers until

solutions are fully deployed, which removes the risk for

smaller customers. Its security model and broad suite

of offerings make it a fit for larger customers, as well.

Radius was a named a Gartner Cool Vendor in 2016.

Industries Represented: Financial services, high tech,

insurance, media, office supplies, travel

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Supported Integrations: Facebook, Marketo, Oracle

(Eloqua), Salesforce

Notable Customers: American Express, Expedia, First

Data, Sam’s Club

Pricing: Starts at $42,000 per year and increases based

on the size of the database and additional use cases

SalesChoice

www.saleschoice.com

Use Cases: Segmentation, account selection,

opportunity scoring, forecasting, upsell/cross-sell

Toronto-based SalesChoice was founded in 2012

but released its first predictive analytics solutions in

2015. The company has an early U.S. patent filing for

predictive and prescriptive sales analytics, leveraging

diverse signals inside and outside the CRM system.

In 2016, it added two new product offerings to its

suite: Prescriptive Analytics and Intent to Purchase

(Propensity Signals). The company is small, with

fewer than 50 employees, and has taken no outside

investment to date. While it recently introduced a

propensity-to-buy module to select accounts to target

for upsell/cross-sell purposes and it can do stand-

alone opportunity scoring, its biggest focus is around

predictive forecasting for companies in the U.S. and

Canada. It uses many diverse machine-learning

methods to identify the likelihood of a win, prescribe

actions and increase the odds of winning. SalesChoice

also leverages AI and predictive constructs to

prescriptively guide sales reps around discounting

and prioritization. Each company gets its own unique

predictive model versus a subset of predictive

attributes, enriching the pattern intelligence.

More than any other company in this guide,

SalesChoice leverages partnerships with Salesforce-

based system integrators, including Accenture and

RelationEdge. It will sell direct, but it can also be part

of larger initiatives led by those partners to improve

sales effectiveness within high-tech companies.

SalesChoice isn’t as visible as some other predictive

forecasting vendors (in part because the lack of

outside investment limits its marketing budget), but it

is a fit for high-tech, professional services and media

companies running Salesforce or large enterprises

that are looking to take on bigger initiatives to improve

sales effectiveness or ABM (particularly for smaller

volumes of accounts).

Industries Represented: High tech, media,

professional services

Supported Integrations: Salesforce (Sales Cloud,

Salesforce1 Mobile App and Wave)

Notable Customers: Accenture, Digiday, RelationEdge

Pricing: Predictive and prescriptive bundle is $750

per seat per year. Intent to Purchase (upsell/cross-

sell) is $360 per user per year plus data fees. Volume

discounts are available.

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TopOPPS

www.topopps.com

Use Cases: Opportunity scoring, forecasting

St. Louis-based TopOPPS has been in the predictive

sales analytics market since 2014. It started off

selling a stand-alone opportunity scoring solution and

later added a forecasting solution. The company’s

core philosophy is to help sales leaders (executives,

operations leaders and managers) enforce better

behavior on the part of sales reps by offering more

accurate information about whether a deal is good

or not. TopOPPS also enables admins to build their

own models. Unlike most others in this guide, the

opportunity score is driven not by likelihood of close,

but whether it’s “healthy.” That health assessment is

done through SFA data but can also use email and

calendar information (through Salesforce, although

not a direct integration). TopOPPS then leverages

prescriptive analytics to suggest ways to improve

the health of that opportunity through embedded

coaching tips and alerts. Information about what

has changed (and why that has impacted the score)

is easily accessible. The company can also provide

metrics to sales leaders around whether a rep is

winning deals at the appropriate rate, selling at the

right price and holding on to deals for too long.

Despite having more than 50 customers, TopOPPS

isn’t as visible or well-known as some other predictive

forecasting and opportunity scoring vendors, While it

certainly sells its ability to improve pipeline visibility

and forecast accuracy for emerging companies,

TopOPPS’ focus around impacting sales behavior by

more accurately representing the health of a given

opportunity and suggesting ways to improve it allows

the company to target a different type of customer.

TopOPPS’ approach is a good fit for large and more

established companies that struggle to change sales

rep behavior because of the long tenure of reps and/

or a culture that is more resistant to change.

Industries Represented: Distribution, high tech

Supported Integrations: Microsoft Dynamics,

NetSuite, Salesforce

Notable Customers: Buckner Companies, Eventbrite,

Interactive Intelligence, TriZetto

Pricing: Starts at $14,000 per year (30 users) and

increases based on additional users

Zilliant

Founded in 1998, Austin, Texas-based Zilliant has

long been known for its predictive price optimization

solution, but it also offers SalesMax, a predictive

analytics application to help sales reps easily

identify what they should be selling to existing

customers. The company is focused squarely on

selling to industrial manufacturers and distributors

that have repeat purchase relationships with their

customers, differentiating Zilliant from many other

vendors providing upsell/cross-sell solutions, and

the recommendations from its models are sent to

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sales reps via email, but are also available via mobile

devices, the web and in Salesforce. Zilliant’s roadmap

includes a number of capabilities slated for later

in 2016 to expand its account-centric approach,

with capabilities to model account-level revenue/

profit potential (based on wallet share) and to better

understand historical performance.

In addition to targeting different industries than

most vendors in this guide, Zilliant also addresses

a different set of sales processes. There are more

than 25 companies using SalesMax, and the average

number of accounts per rep is between 50 and 200.

To cover such large territories, many of those reps

have mandates to visit or call on up to 10 customers

per day. They often look at SalesMax before a call or

in the parking lot before meetings to learn what they

should propose when they get inside. Zilliant claims

that 83% of the opportunities that are presented by

SalesMax get acted on (pursued) by the reps. Reps can

also use SalesMax to identify what customers to visit

for upcoming sales trips.

Industries Represented: Distribution, industrial

manufacturing, high tech

Proven Integrations: Salesforce, SAP

Notable Customers: Dayton Superior, FleetPride, IMI

Precision Engineering, Lincoln Electric

Pricing: Starts at $2,500 per sales per year, with a

minimum of 25 reps

Market Recommendations

IT application leaders should talk with their sales and

marketing stakeholders to understand if there is an

opportunity to use these solutions to overcome more

complex buying processes or to increase the overall

effectiveness of their teams. Despite the buzz, the

market is new enough that many of your stakeholders

may not even know there are predictive analytics

solutions available that they can operate to solve their

problems. And if you work for a larger company, there

may be one of more of these solutions already being

used at a department or business unit level (especially

on the marketing side) without broader awareness at

either the IT or business level.

As you evaluate vendors, accuracy should be only

one of many considerations. While noticeably

worse accuracy would be a potential reason for

disqualification of a vendor during a bake-off, it’s

unlikely that one vendor will greatly outperform

another one. Instead, IT application leaders should:

■ Work with your stakeholders to understand the

dynamics of how your customers buy and how

your salespeople sell. Because in many cases, the

vendors can’t easily differentiate around modeling

techniques and third-party data sources, they

will often try to differentiate around go-to-market

approaches, especially who they target.

■ Look for the vendors that have customers similar to

you (and talk to their references), and pay attention

to what is really important to you from an IT

standpoint and your stakeholders from a business

standpoint. Some vendors are better-equipped to

deal with security concerns, others have modeling

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approaches that favor a particular market type or

culture, others may have better integrations with

key systems, and yet others may provide a better

customer experience after the sale.

■ Clearly understand your own internal capabilities,

especially for more complex use cases. Even

though these predictive solutions abstract the

data science from the users, your stakeholders

may be overwhelmed if they do not receive

adequate guidance from the vendors. Unless you

have internal data science expertise or external

consultants at the ready, ensure that the vendor

you select has capabilities and programs to assist

with that needed guidance.

■ Consider whether ease of use and sales rep

acceptance are important relative to other criteria.

Some solutions have easier-to-use interfaces that

are designed for rapid adoption by sales reps,

but that may come at the cost of functionality or

signal exposure.

■ Ensure that the first-party data you provide to

vendors is high quality, especially for marketing

use cases; while the data doesn’t have to be

perfect and the vendors can cleanse and enrich

to some extent, low-quality data can significantly

impact model performance.

Gartner also recommends, before committing to

automated updates, that you ask vendors to be as

transparent as possible about how their predictive

algorithms work. The algorithms to these solutions

are far more transparent than in the past (a sign

of maturity), but you will still not have configurable

control over the calculation rules or be able to control

the results. The vendors will generally adjust the

algorithms as needed. In fact, some firms provide a

dedicated data analyst to your account — someone

who regularly reviews your results and makes

adjustments as needed. This service is useful for

mitigating the uncertainty that comes from using

a black-box service, but this does not completely

mitigate the risk involved here. Understanding how

the algorithms operate is the best guard against

unexpected outcomes.

Evidence

Gartner conducted interviews with all the vendors

listed in this guide in June and July 2016. This

research was also supported by interviews from

July 2014 through June 2016 with clients and other

enterprises that have implemented those solutions in

the U.S. and Western Europe.

Source: Gartner Research Note: G00303128, Todd Berkowitz, 7 September 2016

AI for Sales Forecasting & Sales Process Execution is published by TopOpps. Editorial content supplied by TopOpps is independent of Gartner analysis. All Gartner research is used with Gartner’s permission, and was originally published as part of Gartner’s syndicated research service available to all entitled Gartner clients. © 2017 Gartner, Inc. and/or its affiliates. All rights reserved. The use of Gartner research in this publication does not indicate Gartner’s endorsement of TopOpps’ products and/or strategies. Reproduction or distribution of this publication in any form without Gartner’s prior written permission is forbidden. The information contained herein has been obtained from sources believed to be reliable. Gartner disclaims all warranties as to the accuracy, completeness or adequacy of such information. The opinions expressed herein are subject to change without notice. Although Gartner research may include a discussion of related legal issues, Gartner does not provide legal advice or services and its research should not be construed or used as such. Gartner is a public company, and its shareholders may include firms and funds that have financial interests in entities covered in Gartner research. Gartner’s Board of Directors may include senior managers of these firms or funds. Gartner research is produced independently by its research organization without input or influence from these firms, funds or their managers. For further information on the independence and integrity of Gartner research, see “Guiding Principles on Independence and Objectivity” on its website.

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