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Dynamic marketing mix modelling

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Dynamic marketing mix modelling

  1. 1. Dynamic marketing mix modelling Advanced analytics for business growth
  2. 2. Service components  Overview  Short-term sales modelling  Long-term brand modelling
  3. 3. Overview  Short-term and long-term models combine sales and brand:  Total financial return on investment.  Optimal short-term revenue and long-term brand building  Short and long-range forecasting
  4. 4. Service components  Overview  Short-term sales modelling  Long-term brand modelling
  5. 5. Economic model structure  Models designed to understand all elements of consumer demand  Journey from online search to website through to off and online purchase  Social media and word-of-mouth incorporated as additional steps  Digital and traditional marketing investments and price impact at each step  Seasonality, distribution, competition and economic conditions act as controls  Fully describes marketing effectiveness
  6. 6. Modelling level  All levels of the business fully captured  All relevant KPIs captured  All category, brand and product cross (halo) media effects captured
  7. 7. Modelling dimensions  Total consumer demand split into component parts  Marketing impact measurement across heterogenous consumer groups  Disaggregated by cross-sections (sales channel, region, product)
  8. 8. Econometric estimation  Econometrics quantifies demand response to marketing and economic drivers  Conventional methods use OLS and distributed lags (adstock) approaches  No allowance for systematic (evolving) variation in product tastes  Ignores persistent or long-term marketing effects by construction  The dynamic time series approach is the solution  Cain, P.M. (2005), ‘Modelling and forecasting brand share: a dynamic demand system approach’, International Journal of Research in Marketing  Cain, P.M. (2008), ‘Limitations of conventional market mix modelling’, Admap, April  Encompasses the standard approach as a special case
  9. 9. Econometric estimation  A dynamic baseline is embedded into the short-term sales model 푦푖푡 = 풙풊풕휷풊 + 휇푖푡 + 훿푡 + 휀푖푡 it it it it      1 1 it it it     1  1 p        t tj t  1 j Model with drivers xit and dynamic baseline μit Model for dynamic baseline Model for trend in dynamic baseline Model for season  Unique time-series decomposition captures short and long-term outcomes  Separates sales into incremental and long-term evolving baseline  Combines offline and online marketing investments in one coherent system
  10. 10. Short-term deliverables Decomposition of business outcomes into component drivers  Unique dynamic base approach captures short and long-term behaviour  Separates business outcomes into incremental and long-term components
  11. 11. Short-term deliverables Short-term marketing ROI  Short-term incremental accurately quantified  Basis of short-term marketing ROI
  12. 12. Short-term deliverables Marketing response curves for go-forward allocation  Demand response to media investment is typically concave  Optimal allocation balances marginal costs and benefits
  13. 13. Short-term deliverables Optimal budget allocation tools  Informs media planning cycle  Combines with model dynamics to guide media flighting strategy
  14. 14. Short-term deliverables Pricing strategy Forecasting and simulation  Price elasticity and optimal pricing rules  Improved supply chain planning
  15. 15. Service components  Total service overview  Short-term sales modelling  Long-term brand modelling
  16. 16. Economic model structure  Marketing investments impact awareness and brand perceptions  Brand perceptions are forged by product experience, driving repeat purchase  Persistent repeat purchase behaviour and lasting shifts in consumer product tastes drive base sales evolution  indicates the extent to which new purchasers are converted into loyal consumers  Lead to shifts in price elasticity as stronger equity reduces demand sensitivity to price change
  17. 17. Econometric estimation  Cointegrated Vector Error Correction (VECM) system explains base sales with brand tracking, satisfaction and other long-term metrics                                                                                                                                  X  X  X  X  X  X   1  1 2  1 3  1 4  1 5  1 6  1  t t 1 2 t t 3 4 t t t t t t t t t t t t 1 2 t t 3 4 t t t X X X X X X X X 5 6 7 7 1 0 0 0 0 0 73 11 31 41 51 61 12 22 52 62 11 12 13 21 22 23 31 32 33 41 42 43 51 52 53 61 62 63 71 72 73 5 6 7 0 0 0 0 0 0           Variables ΔX1t - ΔX7t represent changes in base sales, average price elasticity evolution, consideration, selling distribution, image statements, and media data  System captures long-term dynamic adjustment around equilibrium (cointegrating) relationships within the ecosystem
  18. 18. Econometric estimation  Media impulse simulation quantifies the permanent impact of media campaigns within the VECM ecosystem  TV impulse impact on brand tracking loads into consideration and dynamic sales baseline  Final base impact elasticity cumulates over time
  19. 19. Long-term deliverables  Total marketing ROI includes long-term brand building effects  Informs media copy
  20. 20. Long-term deliverables  Captures all elements of long-term business growth  Rescales short-term response curves to capture long-term impacts  Feeds marketing allocation to build brands
  21. 21. Dr. P. M Cain Managing Director E: pcain@msci-consulting.com M: +44(0)7772107570 www.marketscienceconsulting.com

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