Taking personalization to the next level. Identifying how much personalization to offer – and to whom – will separate winners from losers. More than 70% of customers now expect more personalized experiences with the brands they interact with, and digital technology is enabling companies to meet these expectations by delivering personalization to large numbers of customers at a low cost.
Personalization is not a new concept – companies have for a long time offered users the opportunity to shape or customize products/services, characterized by offerings such as My M&Ms and NikeID. However, another form of personalization is becoming increasingly prevalent, in which customer data is analyzed in real time to deliver more relevant interactions. An example of this type of personalization is Netflix’s Emmy Award-winning engine for generating recommendations, which drives almost 75% of viewing activity on the site.
Customer data and hyper-personalization
As the possibilities for different types of personalization – and the specificity with which it can be delivered – increase, companies are facing a new challenge: deciding how much personalization to offer and to whom. The first type of personalization (customers choosing how to customize their products) usually does not require businesses to access significant amounts of customer data and is largely uncontroversial, especially as customers have usually opted to customize their product or service in the first place. The second type of personalization (delivering hyper-personalized services, offers or recommendations) relies on regular access to customer data and advanced analytical abilities.
Regular access to customer data is a key requirement for most types of hyper-personalization but many customers are reluctant to share personal information. However they reveal a lot more through their impressions and buying history that what they tell would reveal anyway. You need to learn how to win at ecommerce personalization.
2. Draw insights from ‘data’. Different degrees of personalization used to be defined for different customers, but companies begin to appreciate that segmentation is not personalization. Instead advanced personalization requires appropriate software to ensure each individual consumer receives (or sees) what they want, this instead of some marketing quadrant they happened to be able to be classed as, female, 30-40, dog-owner, 2.3 kids etc.
Personalisation will need to change from traditional approaches limited to more sophisticated approaches incorporating digital behavior. Customer data platforms empowered by AI or machine-learning tools, such as SwiftERM, can help companies address individual consumer personalisation in this way at a hyper-personalized level. Insights can be captured in real time to feed back into personalized experiences for individuals that are deemed to offer the higher level of return.
Companies considering investing in personalized offers need to understand the degree of personalization that would maximize lifetime value for each customer.
3. Identify the right level of personalization to offer to individual customers. Identifying whether a large part of your customer base is geared toward hyper-personalization based on their individual data versus customization is only part of the challenge. Understanding and delivering the right level of personalization to individual customers (and at low cost) is where there is greater differentiation scope for businesses.
Analytical tools have been developed to enable companies to rely on techniques such as attribute analysis to map individual customer attributes based on emotive and psychographic factors; event sequence analysis to map and monetise individual customer journey touchpoints prior to and after a purchase; and collaborative filtering to identify information for decision making based on collaboration among multiple data sources.
The final decision on personalization (see Figure 1) will still have to be based on the company’s assessment of the impact of these initiatives on customer lifetime value, and consequently the return on investment in new software tools and processes. In many cases, especially where companies have to choose the level of personalization to offer, this analysis will have to be done at individual customer levels rather than at a larger segment level.