KIE SQUARE PERSPECTIVE
ANALYTICS DRIVEN COMPETITIVE EDGE IN
SHOPPER MARKETING
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KiE Square
KiE Square
– Analytics Driven Competitive Edge in Shopper Marketing
Introduction
For leading Consumer Packaged Goods (CPG) companies across the globe some of the most insistent questions are:
1. Where are they placed in the evolution of Shopper Marketing?
2. How is Shopper Marketing changing in the age of data explosion?
3. Which opportunities should be leveraged to build brands and grow sales?
4. Which capabilities need to be developed to become an industry leader?
This paper aims to answer some of these strategic questions and also understand how analytics is turning out to be a key differentiator in shopper marketing.
CPG companies and retailers are increasingly realising that consumer behaviour is not the only predictor of shoppers’ actions and hence they are now exploring all possible data sources for insights into shopper behaviour. Marketers are beginning to track all the dimensions in the path to purchase like how a shopper navigates through the store (online or hypermarket), layout of the store, packaging of the goods, in-store promotional communication, service and help provided to the shoppers and many more dimensions.
The most recent evolutionary stage in shopper marketing is the rise of shopper solutions. Store merchandising is traditionally done by product categories rather than considering specific shopper solutions. Shoppers typically buy a combination of products and they usually have a solution in mind while shopping. Grouping categories in line with shoppers’ logical selection process triggers their
memory and increases the value of the shopping basket. Today, leaders in shopper marketing are
using shopper solutions to deliver incremental value to shoppers in their shopping and product experience and are able to engage shoppers better.
Shift in shopper marketing
Leaders in shopper marketing today are moving away from category driven shopper marketing to a more holistic solution driven marketing approach.
Chart 1: Marketing Approach Drift
Category driven
1. Linear approach to shopper marketing
2. More tactical
3. Uses syndicated information and databases to understand and interpret data
4. Relying on purely primary research
5. “One size fits all” actions
Solution driven
1. Multidimensional approach to shopper marketing
2. More strategic
3. Interweaves syndicated information and shopper insights
4. Amalgamation of data sources and using a combination of research and analytics for shopper marketing
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KiE Square
KiE Square
– Analytics Driven Competitive Edge in Shopper Marketing
Shopper marketing has evolved from being just one of the ingredients in the marketing mix to a full-fledged science with a potential to drive a strategic impact on the entire CPG & Retail domain. Today, the shopper marketing spending has migrated to include digital elements that enable greater interactivity, direct relationships with shoppers and measurable results. Marketers need to determine at which points on the path to purchase they need to influence shoppers and which instruments would be most effective at each juncture on the purchase path.
Combining consumer insights and shopper behaviour using analytics
Emerging technology and disruptive trends like online stores, social media, interactive shopping through TV, multichannel marketing and fast changing mobile technology are blurring the lines about where shopping starts and where it ends. While technology is bringing in more complexity into shopper marketing, it is also empowering marketers with deeper and richer insights using analytics into the shopper behaviour.
Insights have always been at the core of shopper marketing and they are just as essential to shopper solutions. Marketers are using a range of analytics solutions that are combining consumer insights that drive motivation in brand marketing with the shopper insights that drive action in trade promotions.
One such illustration, in which insights data and analytics have been combined, is about a leading cleaning products company. The company identified a significant opportunity gap between consumer beliefs and behaviours about disinfecting surfaces. 70 per cent of consumers believe that disinfecting surfaces can keep their families healthier, but only 46 per cent act on that belief. The company calculated that the gap between the two represented $400 million in sales. The company sought to understand shopper behaviours around preventive health products. In the process, it discovered that a majority of shoppers prefer to buy these products together and expect to find them in or near the pharmacy.
These insights served as the core of the company’s shopper solution program. The program collected and co-located a variety of multi-brand products including soup, cereal, water filters and
Shopper Driver Models
Assortment Models
Market Basket Models
Product Affinity Analytics
Shopper Solutions
Trade promo optimisation
Strategic Pricing – Price Gap Clustering
Tactical Pricing – Price Elasticity Models
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KiE Square
KiE Square
– Analytics Driven Competitive Edge in Shopper Marketing
disinfectants in a coherent shopper solution around the themes of preventing illness, protecting the
health of family members, and soothing them if they did become sick. Post the implementation of
this shopper solution, the company’s sales grew both at the store and brand levels. The Home Care sales increased by 16% during the program and the Disinfecting Wipes grew a whopping 154%.
Measurement capabilities, especially post-implementation analysis tracking, need to be applied to shopper solutions to determine their effectiveness. Leaders in Shopper Marketing are using analytics to measure sales, profit, market share (by category), number of shoppers (by segment), pre-store activity (by traffic driver), number of shopping visits, spend per visit, and units per visit for both its brand and the retailer brands included in the shopper solution. Analytics also allows companies to benchmark its efforts against close and near competitors, and the CPG industry overall.
Ready Data Pool for Seamless Business Impact
A constant challenge for CPG companies is that they do not own a majority of data streams that are a key to operational and strategic decision making. Therefore, there is an intermittent need to reconcile and amalgamate these various sources of information and link them seamlessly to the business decisions.
Chart 3: Mapping Data and Solutions to Business HotSpots – DPRM Strategy
AC Nielsen Retail Audit
Panel Data
Monthly Track Data
Internal data on Campaigns -TV, Promotion, Pr int
• Market Mix modeling • Promotion/Distribution /
Pricing Optimization • Channel Management • Market Share Analysis • Cannibalization Impact
& Portfolio Balancing • Market Basket Analysis • Loyalty Analysis • Brand Equity Drivers (SEM/PLS Approach) • Channel Management
• Media Optimization • Promotion Optimization
Disaggregating trends Optimizing the portfolio Building the Brand Salience
Needs Overlay
Better Planning & execution
Relevant Solutions
Need to be aligned with Sales Data & Causality is tracked
Need to be analyzed for effectiveness and
optimization
Impact
Data Pool
Retail Audit Data
Proactive Market Sensing & Execution
•Precise Volume Decomposition
•Impact of all initiatives & its components is known
Strategic Decision Aid •Portfolio Weights vis-à-vis competition
•Synergistic marketing
•Brand & Category Stability indicators
Brand Management •Brand positioning, salience & targeting through robust models
Marketing Execution •Higher effectiveness
•Big size of prize
•Greater ROI
Need to be aligned with strategic decision
making Needs to be systemized & invested
into
The organizations that have been able to create efficient pools of data and overlay them on day-to-day and strategic business goals have been able to maximize the ROI on each dollar spent for data related costs. KiE Square has evolved an elaborate Data Pooling, Reporting and Mining strategy (DPRM) and recommends the same to its clients for optimal gains.
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KiE Square
KiE Square
– Analytics Driven Competitive Edge in Shopper Marketing
Data Led Proactive Action is the Key
There are three scenarios for corporate response to a market event - Reactive, Concurrent or Proactive. Most of the organizations have built strong capabilities in responding to category events that happened in the last quarter and some have travelled the distance to even make it concurrent. However, there are very few organizations who have invested in the capability to anticipate the event in advance and estimate its impact on overall volumes and market shares. The gains of such a strategy are manifold as also highlighted in the illustration below where the CPG industry player could make six times greater volume impact through the two-quarter proactive action than the one-quarter reactive action. The reliability of the estimation over time clearly demonstrated that proactive actions are scientific and provide a clear competitive advantage even in a complex category.
Chart 4: Impact of Data Led Proactive Actions on Volumes and Profits
Future Trends and challenges Big Data
Many marketers are still trying to solve the problem of information overload.In the future, the challenge will be to make big data analytics into meaningful actions.
Social-Media Shoppers are now becoming smart and are increasingly becoming reluctant to share their personal information on the social media. Shopper marketers will have to innovate their ways to capture meaningful information on social media through analytics and weave it with shopper models.
Considering the situation of economy dipping down by 5%, Impact of counter action of strengthening distribution width by 5% with various reaction time:
Scenario Name 2011_Q3 2011_Q4 2012_Q1 2012_Q2 2012_Q3 2012_Q4 Year 2012 Volume
Incremental Benefit
No Action Taken 11,076 11,459 10,893 11,360 11,394 11,436 45,083
-Reactive Action 11,076 11,459 10,893 11,360 11,394 11,660 45,307 0.5%
Concurrent Action 11,076 11,459 10,893 11,360 11,618 11,818 45,690 1.3%
1 Qtr Proactive Action 11,076 11,459 10,893 11,585 11,777 11,875 46,129 2.3%
2 Qtr Proactive Action 11,076 11,459 11,117 11,743 11,834 11,895 46,589 3.3%
Economic Dip 10,200 10,400 10,600 10,800 11,000 11,200 11,400 11,600 11,800 12,000
2011_Q3 2011_Q4 2012_Q1 2012_Q2 2012_Q3 2012_Q4
No Action Taken Reactive Action Immediate Action
1 Qtr Proactive Action 2 Qtr Proactive Action
0.5% 1.3% 2.3% 3.3% 0.00% 0.50% 1.00% 1.50% 2.00% 2.50% 3.00% 3.50% 4.00% Reactive Action Immediate Action 1 Qtr Proactive Action 2 Qtr Proactive Action Incremental Benefit 2 Quarter proactive action has yielded 6 times more impact than the reactive one Modern Trade
No Action –No counter action taken against economic dip
•Reactive action - Action Taken , but one quarter later
•Concurrent action –Action taken in the same quarter
•Proactive actions - Action taken proactively 1 quarter and 2 quarters in advance
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Proactive Action (Q-2) protects 6 times more volume than Normal Manufacturer Response (Q+1) to unfavorable macroeconomic changes while it enhances volumes by over 6 times in case of favorable economy
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KiE Square
KiE Square
– Analytics Driven Competitive Edge in Shopper Marketing
Mobility Marketers are expected to continue their efforts in integrating mobile devises into the shopping experience. The app revolution in mobile devices is also likely to play a crucial role in shopper marketing.
Deals
In these uncertain economic times, shoppers will be looking for better deals and more entertainment. This means that marketers will become more creative and will listen more to what shoppers need. Shopper Analytics will have to be leveraged in testing the most suited deals and offers and at the most appropriate juncture in the path to purchase.
Conclusion
While many organizations use analytics, most struggle to execute an analytics strategy that results in meaningful actions because they are immersed in compiling data and conducting fragmented analysis. CPG companies and retailers need to collaborate among them and embrace organized Analytics to transform Shopper Marketing in their quest to attain leadership in the space. There needs to be a clear analytics strategy for consumer level interventions and category or segment level interventions.
About KiE Square
KIE Square is a leading provider of advanced analytics and data mining solutions that help companies improve business decision-making across the enterprise leading to profitable outcomes. We provide advanced analytical solutions in the areas of marketing decision optimization, CRM analytics, consumer insights, risk management and pricing optimization.
Contact:
www.kiesquare.com
India: +91 -124 - 4046008/09 Email: [email protected] US: +1 (302) 635 1234 Email: [email protected] Singapore/ Malaysia: +60 108934911 Email: [email protected]
