Big Data in Retail Taking the next step with System Dynamics. Bob Eberlein

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1 Big Data in Retail Taking the next step with System Dynamics Bob Eberlein isee systems

2 The Value and Promise of Big Data Unlocking customer needs Fact based ddecision i making Knowing what works Who it works on Finding out more and more Measureable activities are everywhere

3 The Big Picture with System Dynamics Changes propagate Customers evolve customers Suppliers respond suppliers Competitors adapt System Dynamics brings it all together competitors

4 Setting the Stage Buymore is a large electronics store chain Sales have been stagnant Largemart, their competitor, has been gaining market share TLA, a Big Data consultancy, is pushing a system to help target customer outreach B b t d t f S t D i i h i t Bob, a student of System Dynamics, is hoping to add insights around TLA s design

5 TLA s Pitch to Buymore We can identify customers responsive to marketing efforts Target them, ignore the unresponsive

6 Bob s Reaction That s really cool Cause it is You have great inputs predicting response Demographics Location Time of year Product novelty But there is more than a list

7 From Lists to Stocks Stepping behind the curtain What causes that? Why do things change? A simple thought Buying needs recharge 1: purchases 2: promotion effectf ect building needs normal need f ullf illment rate latent needs purchases promotion effect Page Months Long Term Customer Responce

8 Implication for Data Analytics Response effects will change over time Even when the inputs are not changing We measure what has happened We want to know what will happen That is the value of structure But, one of the TLA crew thoughtfully says Buymore has thousands of customers you can t just model one.

9 Getting at the Detail Different, but not that different Bob replies We can capture that with a bit more detail normal need f ullf illment rate tendency to purchase promotion effect building needs latent needs indiv idual purchases purchase size 1: indiv idual purchases[1] purchases Periodic purchasing Page Months One Customer

10 Adding things up With 1,000 customers Each purchasing periodically Together we get: Same pattern 1: purchases 2: promotion ef f ect Page Months 1000 Customers

11 Why d that work? The TLA gang think it is a coincidence But it is a bit more Same feedback structure Purchases change latent needs which influence purchases The individual model just has more detail The behavior adds up Feedback insights always do Not always true with bottom up

12 Anything we can use? The data scientists are now curious Bob has to XMILE Two approaches Use the models offline Efficiently developed using System Dynamics Translated for use in operational systems Tricky, but doable Incorporate the models directly That is the promise of XMILE

13 XMILE at the front end XMILEwill support integration withstandard XMILE will support integration with standard tools

14 System Dynamics Inside Big Data Discrete choice models are already there Logit and Probit analysis Constrained by the efficiency of design System Dynamics simplifies this process Especially working the conceptual case The resulting models can be used directly Hand coded Or with XMILE supporting an engine

15 Taking a step back System Dynamics is a helpful framework for conceptualizing micro structure But it shines with the Macro Buymore wants to know what will happen when the new analytics are turned on. Customers Suppliers Competitors

16 The Straight Arrow View targeting based on analy tics adv ertising spending ads reaching responsive people sales ads reaching unresponsiv e people Change who we hit Get lasting results Largemart no change 1 1: sales[us] 2: sales[them] Page Months Simple Mental Model

17 Refining the Customer View Refresh dynamics Rebuilding Influence that Change demand customer ref resh time Direct impact underly ing customer needs rebuilding customer needs effect of adv ertising on ref resh f requency met customer needs total purchases time to f ulf ill needs demand shipments effect ect of adv ertising on purchases

18 Customers Respond We gain, but diminish Catch the low hanging fruit first But our competitors? 1: shipments[us] 2: shipments[them] Page Months Untitled

19 Why do we help Largemart? First we are quickening the refresh rate Focusing on the most responsive customers Getthem them to upgrade faster Sometimes they upgrade at that place Second, we have given up on one population They become low hanging fruit for Largemart Whose ads continue to reach them Their market share goes up on the unresponsive customers

20 And what about our suppliers? They need to be ready demand in process ready stock orders receipts shipments process time stock correction ~ ef f ect stock sales av erage shipments adjustment av shipments

21 Or the response can be uneven Giving our competitor a bump 1: shipments[us] 2: shipments[them] Page Months Untitled

22 Big Data is a Good Thing Same spending on ads Better sales as a result But identifying the best customers is only the first step Everything will evolve after we make changes We can anticipate that evolution A few first steps are shown here

23 Tracking ongoing changes The power of Big Data is to help implement new systems integrating the data and analytics Ongoing sources of insights Continually refined with renewed data That same implementation can be done with System Dynamics models dl What stands in the way is the lack of a Standard

24 Integrating Both

25 Observations Many very standard components Supply chains Market segmentation Relatively simple models Capturing aggregate relationships Focused on feedback action and reaction Anticipating dynamics

26 XMILE An XML standard for representing System Dynamics models Supports integration with other toolsets Encourages more big data applications Allows models to be incorporated into: Standard analytic tools Enterprise support software

27 The XMILE Technical Committee A committee of the OASIS standards body Working on the specification Based on an existing iti draft specification Expected to publish in the next months. Looking for input Industry and application specialists Technical people seeking integration open.org/committees/xmile open.org/committees/xmile

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