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JPK
Gro
upBusiness Forecasting and Analytics Forum
September 19-20 • Chicago, IL
In-Depth Workshop:Digital Forecasting and Analytics
September 20, 1:15pm
Widely considered one of the leading digital measurement experts in the world, Garyleads EY’s Digital Analytics Practice. EY acquired Gary’s previous company –
Semphonic – in March of 2013. As Semphonic’s President and co-Founder, Gary ledSemphonic’s growth over a 15 year period from a 2-person practice to the one of the
leading digital analytics practices in the United States. Voted the most InfluentialIndustry Contributor by the Digital Analytics Association in 2012, Gary writes an
influential blog (http://semphonic.blogs.com/semangel), has published more thantwenty whitepapers on advanced digital analytics practice and is a frequent speaker
► Exponential Smoothing applies a weight to each iteration,
constantly adjusting the forecast score against the
difference between the previous forecast/actual. The
weight determines the amount of adjustment:
Forecast Visits
Feb. 2016
1,671,7261,200,000
1,400,000
1,600,000
1,800,000
2,000,000
4/1
/20
14
6/1
/20
14
8/1
/20
14
10
/1/2
014
12
/1/2
014
2/1
/20
15
4/1
/20
15
6/1
/20
15
8/1
/20
15
10
/1/2
015
12
/1/2
015
Actual Exponential Forecast
1671726
.5 Weight to Actual and .5 Weight to Forecast
Page 19 Introduction to Digital Forecasting
Exponential Smoothing in Excel
► Excel includes a simple Exponential Smoothing function in
the Data Analysis Toolpack (an add-in):
Page 20 Introduction to Digital Forecasting
Double exponential smoothing (Holt)
► Double exponential smoothing adjusts both the predicted
value and the trend (weighting) with each new value.
► This makes it much better for matching trends than Single
Exponential Smoothing.
Forecast Visits
Feb. 2016
1,790,276 1,200,000
1,400,000
1,600,000
1,800,000
2,000,000
1/1
/20
15
2/1
/20
15
3/1
/20
15
4/1
/20
15
5/1
/20
15
6/1
/20
15
7/1
/20
15
8/1
/20
15
9/1
/20
15
10
/1/2
015
11
/1/2
015
12
/1/2
015
1/1
/20
16
Visit Double Exp Forecast
Alpha 0.9
Beta 0.2
Page 21 Introduction to Digital Forecasting
Double Exponential Smoothing in Excel
► Here’s the process for double exponential smoothing in
Excel:
1 Set your alpha (exponential smoothing value) and beta (trend value) – between 0-1
2You will create three new values: Exponential Forecast, Trend Forecast, and DESmoothed Forecast (this last one is the real forecast)
3 DESmoothed Forecast is always equal to the Exponential Forecast + Trend Forecast
4For the 1st Period, the Exponential Forecast is equal to the actual value for that period and the Trend Forecast is zero.
5For every other period, the Exponential Forecast is equal to the Previous Exponential Forecast plus the alpha value times the difference between the Previous Actual and the Previous DESmoothed Forecast.
6For every other period, the Trend Forecast is equal to the Previous Trend Forecast plus the beta value times the difference between the current Exponential Forecast and the Previous DESmoothed Forecast.
Page 22 Introduction to Digital Forecasting
Double Exponential Smoothing in Excel
► It looks like this:1
2
3
3
4
5
5
Page 23 Introduction to Digital Forecasting
Double Exponential Smoothing in Excel
► It looks like this:6
6
Page 24 Introduction to Digital Forecasting
Triple exponential smoothing (Holt-Winters)
► Triple exponential smoothing adds a weight for a seasonal
cycle. The predicted value and trend value are updated
identically to the Holt method except that they are first
adjusted seasonally. The seasonal parameter can be
tuned or pre-determined.
1,200,000
1,400,000
1,600,000
1,800,000
2,000,000
Actual Forecast
► It should only be used
when your data has a
significant seasonal
component.
Page 25 Introduction to Digital Forecasting
About those pesky parameters
► You can use tools (like Excel’s Solver) to find the best
values for the parameters.
► You do this by calculating the MSE (average of the
forecast errors after squaring) and then optimizing the
parms to that value.
Page 26 Introduction to Digital Forecasting
Break-outs and Banding
► Break-outs and banding aren’t a separate forecasting
technique – they are tools for understanding whether a
movement is interesting.
► All processes have a certain amount of variation. Banding
is used to draw a band of fairly natural variation around
the trend. When an actual “breaks” the band, the variation
is usually significant.
1,200,000
1,400,000
1,600,000
1,800,000
2,000,000
2/1
/20
15
3/1
/20
15
4/1
/20
15
5/1
/20
15
6/1
/20
15
7/1
/20
15
8/1
/20
15
9/1
/20
15
10
/1/2
015
11
/1/2
015
12
/1/2
015
1/1
/20
16
Actual Upper Band Lower Band
Page 27 Introduction to Digital Forecasting
Whew…Let’s take a break and then tackle Modeling
Page 28 Introduction to Digital Forecasting
Model-based Forecasting
► Outline
► Time-Series vs. Models
► Identifying key variables
► Source, Season
► Building a Conceptual Model
► Sample Conceptual Models
► Quick Discussion of other Models
Page 29 Introduction to Digital Forecasting
Time-Series vs. Model
Forecast Model
Page 30 Introduction to Digital Forecasting
Building a Model
► The first (and maybe most important) step in building a
model is deciding what variables you might use.
► Keep in mind that there is no one right answer. The level
of variables you use needs to match the operational level
you want to understand.
► For example, if you’re trying to optimize channel
marketing decisions, it doesn’t work to use Total Marketing
Spend as your marketing variable.
Page 31 Introduction to Digital Forecasting
Throwing variables at a wall
► Don’t just toss variables into a modelling blender.
Page 32 Introduction to Digital Forecasting
Building a conceptual model
Website Traffic = Visits from last month * Repeat Visit Rate( ) + Avg. New Visits
Website Traffic = Search Visit per Dollar * Exp. Search Spend( ) +
Display Visit per Dollar * Exp. Display Spend( ) +
Avg. Direct Visits
Website Traffic = Current High Frequency Customers * Avg. Visit Propensity( ) +
Avg. New Visits
Current Med. Frequency Customers * Avg. Visit Propensity( ) +
Current Low Frequency Customers * Avg. Visit Propensity( ) +
Page 33 Introduction to Digital Forecasting
SEO
Page Ranks
Keyword Volumes
Outcomes
Open Sessions
Repeat Rates
Satisfaction
Exogenous
Econometrics
Brand Awareness
Web Growth
Device Shifts
Seasonality
Sample systems and variables
Marketing
Total Spend
Digital Spend
Channel Spend
Mix
User-Base
Active Users
Users by Cohort
New Users Last Period
Segmentation
User Types
Visit Types
Page 34 Introduction to Digital Forecasting
Deepening a conceptual model
Website Traffic = Visits sourced by marketing +
Visits sourced by our user-base +
Visits sourced by Web “Flow”
Visits sourced by marketing = Total Marketing Spend * constant
Visits sourced by marketing = Digital Marketing Spend * constant
Mass Marketing Spend * constant
+( )
( )
Visits sourced by marketing = PPC Marketing Spend * constant
Mass Marketing Spend * constant
+( )
( )
Display Marketing Spend * constant +( )
Page 35 Introduction to Digital Forecasting
Deepening a conceptual model II
Visits sourced by marketing = PPC Marketing Spend * constant
Mass Marketing Spend * constant
+( )
( )
Display Marketing Spend * constant +( )
► Implies that the impact of increasing PPC marketing
spend will be constant. That’s rarely the case for any
variable except over a narrow band.
► We could further break out PPC Marketing spend into
categories (like brand, non-brand) but eventually we’ll run