It’s no secret that Consumer Packaged Goods (CPG) companies without significant direct-to-consumer (DTC) sales or direct ownership of retailer purchase data are typically challenged with gathering accurate and timely data on consumer purchases. Most brands rely on third-party data, either supplied by the retailer at their discretion or research firms in aggregate, neither of which allows CPGs to build a relationship directly with their customer or to gather insights beyond transactional data.
Now more than ever, CPG brands need direct consumer data to better understand consumers and what motivates their behavior, inform decisions on product placement, distribution and consumer engagement, and to quickly adapt to changing consumer shopping behaviors.
That’s why, increasingly, CPGs and FMCGs turn to loyalty marketing for a treasure trove of purchase data and other highly useful consumer information, in order to build their own direct-to-consumer relationships and to create their own data assets with an identifiable consumer. Notable among these, PepsiCo, Kraft and Nestle recently established DTC channels. In addition to fighting decreased foot traffic of traditional retailers during COVID-19, these brands can take control of the consumer relationship and fight off industry threats by leveraging data from loyalty programs to improve the consumer experience and drive incremental revenue. Recent developments in data capture and analysis provide more opportunities for CPG companies in the loyalty space than ever before.
Below is a guide on why and how to leverage direct consumer data.
Building Data from Consumer Purchase
The Opportunity: Gathering purchase data on your brands and relevant categories outside of your brands can identify evolving shopping behavior and purchase patterns across segments. This direct data also illustrates potential early shifts that represent threats to market share and business growth. In addition, data on purchases through receipt scans can uncover opportunities to develop promotions or campaigns to target various individuals or segments to effectively upsell, cross-sell or win back consumers in a more targeted, personalized and efficient manner.
The Science: Providing direct information on consumer purchases, a core construct of a CPG loyalty program includes the proof of purchase mechanism. Enhancement in receipt scanning technology to provide proof of purchase information not only makes it relatively easy for consumers to earn loyalty program currency, but also provides CPG brands with direct data on purchases. This is in addition to other proof of purchase methods, such as code on pack, which can provide direct and detailed information on a consumer purchase, but which can sometimes be costly to implement. Details gathered on purchases, such as retailer, SKU, location, time of purchase and purchase price, provide information in near real-time on what your consumers are purchasing from your brand(s), which can help supplement or the third-party data sources that consumer brands typically rely upon.
In addition, receipt scanning delivers insight into the full basket of shopping purchases (including competitive brands), purchase activity, and patterns of adjacent or other relevant categories. This provides rich, added insights on your consumer purchases that are typically unknown.
Creating Consumer Profiles and Preferences from Direct Data
The Opportunity: Marketers should ensure their request for consumer information is made in a way that clarifies to the consumers how providing their data will benefit them through an improved brand or program experience. In this way, consumers see the value in providing their data, as well as how their data will be used and protected by the CPG brand. In a world where government data regulation is increasing and privacy and data concerns are growing with consumers, this isn’t just prudent, it is critical. Profile and survey data can help with segmentation and personalization of messaging and offers at a targeted 1:1 level. Plus, at a more macro level, these data points can shed light on demographic makeup, consumer traits and product needs through data patterns and shifts identified via program-generated consumer data. These insights can inform decisions beyond just marketing and loyalty, they affect everything from product development, media targeting and program optimization. For example, when one of our cosmetic clients was facing the closure of its largest retail location, we used loyalty data to identify at-risk customers, targeted them with highly personalized offers and directed them to the next best store location, resulting in an over 50% sales retention from high-risk segments.
The Science: Loyalty incentives not only help encourage consumers to opt-in for your program and direct brand communications, but also motivate them to provide additional information about themselves via profile completion in exchange for program points and/or rewards benefits. Consumers are more willing to provide their personal data (as long as they see it being relevant to program interactions) in exchange for something that can benefit them. In fact, more than 75% of respondents to a recent Accenture survey said they were willing to share data for a more personalized experience, efficient and intuitive processes, and competitive pricing. This provides a clear value exchange for the consumer that can be difficult to establish in other channels. Through this value exchange, consumer preferences, needs, feedback and interests, which may guide brand and program activities and enhancements, can be collected through surveys, quizzes and other interactions through your program by providing loyalty benefits. A valuable offer of bonuses or other program benefits (e.g. brand discounts or free trials) means consumers are more likely to give these deeper data points to CPG marketers in exchange for a better experience. Reflecting the right value exchange is the key to success – i.e. more points or higher value benefits are required the more in-depth a survey or questionnaire is to ensure it is worth their time. However, nothing will alienate a consumer faster than a brand collecting valuable data it doesn’t use. Make sure your data feeds directly into customer profiles to be leveraged by deep segmentation, which will drive personalized offers. For example, for a cosmetics company, if you collect data on preferred colors and shades of products, don’t send you consumer offers on products they won’t like or even be able to wear.
Driving and Optimizing Program Performance
The Opportunity: Loyalty program KPIs and testing results gives marketers a highly nimble channel to test multiple elements, measure effectiveness and optimize for increased performance. Overall, loyalty is one of the few channels where the CPG marketer can directly assess marketing ROI and revenue impact.
The Science: The nature of loyalty, with tangible consumer benefits that drive continuous engagement, means consumer transactions and interactions provide direct and agile program performance data over other marketing channels, giving marketers an environment to continuously test and learn. This guides segmentation and boosts personalization for a better, more relevant consumer experience. Establishing test scenarios (A/B testing, exposed vs. control test, etc.) with marketing messages, promotions and segmentation/personalization approaches can help brands effectively quickly identify best approaches and optimize for maximum effectiveness.
The Power of Predictive Data Analysis
The Opportunity: Models crafted from collected data points can identify consumers’ future journeys, those most likely to churn, and next best experiences for effectiveness and engagement. For example, consumers can be scored both on their probability to churn and propensity to be your top advocates, with predictive algorithms that can be used for smarter segmentation rules to drive campaign and communications that have the most impact to the bottom line. Predictive data analysis can provide better guidance on program and campaign opportunities to intervene in the case of negative patterns and/or to effectively convert on opportunity areas. In a test for a large global CPG company, we ran a churn analysis which was used to drive campaign activity as a test against more traditional rule-based approaches to a win-back campaign aimed at lapsing members. Use of the churn analysis data resulted in 66% lower campaign costs while driving 11% higher ROI, and was 7x less likely to present mis-targeted offers over the rule-based group.1
The Science: Loyalty programs offer a channel to generate unique and consistent data points not only for current insights but for better anticipating, quantifying and predicting the future. Inputs from program data points such as transactional momentum, gap and rate of change metrics, geolocation propensities, and behavioral segments and clusters can feed models for highly accurate projections. AI and machine learning capabilities enable this type of analysis to be run more frequently and efficiently than ever before, analyzing millions of variables to deliver the most optimal insights. Purchase data together with program interaction data and patterns can provide key insights for predictive analysis on things like consumer churn, next best experience, future purchase scenarios and associated revenue potential. These types of predictive analyses, established with program data points gathered over relevant business cycles, unleash valuable insights into consumers’ future propensities and areas of opportunities to save and generate revenue. From here, predictive models can be translated to illustrate future revenue patterns – both areas of risk and potential incremental opportunity – to help assess areas for best program investment.
A caution – as we’ve seen in recent months during COVID-19, the algorithms have been taxed as consumer behavior models weren’t prepared for a spike of panic buying of toiletries and other CPG essentials as an example. It’s always a good practice to have a model monitoring practice in place to ensure your models don’t fall victim to model drift due to the underlying data varying drastically from the data it was trained on. If this happens, the predictive quality of the model(s) will be adversely affected.
Smart loyalty strategies and new technology developments can overcome many of the historical challenges faced by CPG marketers by efficiently generating direct consumer data and insights from receipt scanning and basket analysis to AI and machine learning.
Increased effectiveness, strong ROI and incremental revenue opportunities can all be realized by tapping into loyalty constructs to drive data generation and optimization.
1 Internal Kognitiv benchmark data, 2019