Consumer pricing strategies to overcome blind spots

Many consumer-focused organizations continue to rely heavily on internal data (or a narrow set of external factors) when analyzing consumers. By limiting their view of consumer activity and preferences, companies risk missing key trends and opportunities developing outside their own “four walls.” While more than 90% of retailers today can pair basic customer information (often from loyalty programs) such as name, contact information, and birthdays with internal spend data, this information may fail to detect deeper consumer demand patterns and/or opportunities for growth.

Organizations often default to static assumptions around competitive differentiation, the value of specific product attributes or characteristics, and a consumer’s willingness to pay for goods and services. Such measures have historically been based on a combination of information sources, such as competitive benchmarking, vendor recommendations, internal pricing tools, and traditional consumer research. These sources often treat consumers as broad, homogenous segments or are slow to “update” based on changing conditions. As a result, pricing-related actions may not be sufficiently granular nor fully reflect nuanced changes in consumer preferences, a product’s position relative to competitors, and the true level of differentiation established in the market.

Many consumer-focused companies continue to set prices and promotional offers at a national level, limiting their ability to capitalize on localized or individualized growth opportunities. Large price zones will cut across localities or consumer segments, which often have varied preferences and levels of product demand. This results in price setting “to the average,” thereby over- or undercharging consumers depending on the granular demand shifts. Similarly, mass promotions offered across customer segments will struggle to activate diverse customers effectively and result in suboptimal ROI. A continued strategy of pricing and promoting with uniformity is likely to result in untapped growth potential.

Advanced consumer organizations are enriching internal data with a variety of external data sets—seeking to create a 360-degree view of the consumer beyond an organization’s “four walls.” Such data sources include credit/debit card spend data, cellular mobility data, third-party demographic and income data, and behavioral/activity data (e.g., gym memberships). External data should not serve to replace valuable first-party information, but rather it should complement and enhance a company’s current decision-making process. Companies are leveraging the combined data sets for a variety of use cases, such as:

• Enhancing consumer segments: Merging CRM/first-party data with credit card, demographic, income, and localized economic data to create more refined consumer cohorts and associated pricing strategies.
• Understanding cross-purchases: Developing a view of the full consumer basket to identify new opportunities to increase share of wallet, cross-promote, or identify third-party partnerships.
• Driving loyalty: Crafting messages that speak directly to a consumer’s lifestyle or need state, thereby strengthening the brand/consumer relationship and enhancing pricing power.

Leading organizations are using both advanced modeling on transactional data and rapid voice-of-the-consumer studies to better understand and quantify how consumer perception of value is shifting and how their products are differentiated from competitors. This process can, in turn, help ensure companies are maximizing value capture through pricing actions. For example, AI-enabled demand models are being applied to first-party sales data to “decompose” and identify product attributes consumers find the most attractive. These mathematical demand models can be combined with tools such as rapid direct market research to further complete a quantitative/qualitative view of consumer preferences.

The most sophisticated consumer-focused companies are parsing data at a granular level to understand individual, location-specific consumer patterns. This data is combined with algorithms and machine learning to target consumers with localized pricing and/or personalized one-on-one promotion. However, many organizations are still pricing at a national (i.e., “one price”) level. The advanced use of internal and external data sources and clustering models can determine the most significant attributes of localized demand and then group consumers and/or stores based on these attributes. This facilitates the process of localized pricing or assortment decisions and better matches value perception to unique consumer preferences.