Being customer centric is a changing game. In today’s world of social media, we have a great platform for companies to create multiple variations of ads, optimize them over time, and identify the best channel and timing. This is a big step beyond personalization, but does this mean that all companies who do this are actually customer-centric?
Being Customer centric enables companies to understand who their users are, why they are using this product/service, what the benefits for them. All these questions need to be assessed before starting to design a solution or ad.
This process may, at times, seem too philosophical and time-consuming, but there is a simple question that companies can ask themselves, such as “Does our product/ service have a different value proposition for different users? A proposition, in this context, means a single-minded message that explains “what’s in it for me”, why your users would like the product/service. For instance, a navigator app. Tourists and travelers might like it as it helps them explore new places easily, while business people might prefer it as it gives them a worry-free feeling when the apps select the fastest route to their work and help them avoid traffic jams. Therefore, when evaluating your ad performance, it’s not just about the ads creative aspects, but also the ad’s proposition that drives most attachment.
Once we realize which proposition is most effective for each user profile, we can open a meaningful channel of communication that can be used at different points of contacts throughout the customer’s lifecycle. This methodology combines the power of machine learning and business understanding, and should work in the following manner:
Analyze your existing database and market research to identify profiles of different users
Determine the value proposition for each user profile
Create the ad’s creative based on the proposition, and add an additional neutralized version as a control and, in order to review whether a certain proposition makes a significant difference
Use machine learning to create different permutation, and then identify the most successful ads and channels
Analyze campaign results and then design customer journeys, based on your findings
For example, take a service that looks for people who wish to work as pet sitters. Try different wording and designs and see which ones work best.
Alternatively, try to divide your target audience based on the proposition, such as mums who want to make some extra money while taking care of their kids, parents who are looking for a job for their teenager children, teenagers who love animals, or teenagers who want to earn money.
For each segment, develop a specific messaging and tone of voice. To make things easy for yourself, try mapping all the different profiles on a matrix that classifies the relationship between the app and the user segment, based on a scale ranging from “emotional” to “necessity”, to create better messaging. Once this stage is complete, create an additional set of neutral messaging that could be used across all profiles. The next step is launching the campaign. Then let the numbers tell the story.
Instead of just being able to identify the best ad, you will now be able to even pinpoint why this is the best ad, and use this information in various interactions for increasing customer engagement, call to action, and word of mouth.