Despite having all information about their customers, brands usually focus only on traditional ways of marketing like product marketing or channel marketing. The marketing folks belonging to the product team and channel teamwork in silos and do not collaborate to achieve the brand’s vision. So SEO team doesn’t share their campaign information with paid media who in turn don’t share their results with on-site optimization team. Similarly, in a CPG company product owner for a mobile phone would not share their marketing plans with the laptop product owner. This seriously limits the usage of data thereby limiting returns from marketing spends because they may essentially be targeting the same customer without understanding his/her needs. The end result is that marketing function becomes a driver for achieving short term goals like increasing leads, getting more traffic & increasing product engagement without any meaningful action.
For last few years, in addition to this myopic view, brands are desperate to integrate customer information from various data sources to get an integrated user view in a single system like DMP with little or no knowledge of the end goal of doing the same. To achieve this, either they already have or are in the process of making large investments in setting up a DMP system. The problem which is prominent is that once the system is in place, marketing teams either get a suboptimal return on their investments or don’t get any return at all. The reason for this is the absence of a clear road map on how to utilize a DMP for marketing in a consistent way. The fallback is that brands engage with consultants from DMP product companies to get ‘value realization’. These consultants work on use cases to show the value of a Mar Tech/Ad Tech integration or a product functionality rather than enabling clients for long term sustainability. This increases the client’s dependency on product consultants to continuously show value to the stakeholders of the large investments done on DMP.
To overcome these two challenges it is imperative for the marketers to think which customer problem they are trying to solve for first. For that to happen, they need to know who really their customers are and how they behave in an ever-connected landscape. Once that is defined it has to be communicated down to the last person facing the customer or trying to reach out to the customer on a brand’s behalf. The actual scenario, however, is that SEO, paid media, sales team or analytics teams do not know how their customers look like. To top this, customer aspirations, behaviour and attitude are continuously evolving with technology.
Customer personas enable marketers to understand customer’s aspirations, behaviour and attitude for narrowing down their needs, preference and likelihood of responding to a marketing message. To elaborate, suppose Hyundai wants to execute a campaign for increasing leads generated through the online channel for the new Santro. Two personas of the target audience for Santro would be working mothers who need a city car for a ride to the office and housewives who need a city car to run daily errands. The typical profile of these personas in India would be.
Working ladies who need a car to drive to the office:
- Age 25-55, Income Rs 500K+, Married/Single
- Likes to spend on herself and has an independent thinking
- Likes reading lifestyle sites, a financial newsletter, etc.
- Browses internet through office laptop, her mobile phone & tablet while at home
Housewives who need a car to run daily errands:
- Age 25-55, Household Income Rs 1.5M+, Married
- Housewife, financially conservative
- Spends time on lifestyle sites, social media, coupon sites, etc.
- Browses internet through home desktop and her mobile phone.
These personas can be further elaborated but this would have given a fair idea of 2 customer personas for Santro. Next would be identifying the data sources which have audience attributes for these personas. It may be 1st, 2nd or 3rd party data sources. Once a DMP set up is in place, usually creating segments for these personas is a relatively straight forward job. To measure the success of targeting these personas, media campaigns have to be tagged comprehensively and integrated with DMP for aggregating information from all the campaigns.
How does this all help in increasing return on marketing spends and meeting long term marketing goals? Once personas are defined for all product categories with a high level of granularity marketers can decide on how much media budget has to be allocated to each persona/product. To do this they would need to get the following information:
- The size of the addressable segment: Extending the 2 personas for Santro car customers let’s assume that there are 50 million unique audiences in the segment of working ladies and housewives. Further, assume that the DMP has integration with only a DSP and it can say reach 5 million out of these 50 million. Hence the addressable segment would be 5 million and not 50 million because that is what the marketers can reach using Display Ads.
- The propensity of the segment to respond to a marketing message: This usually can be obtained from historical data. In this case, let’s assume that the aggregate response for DSP is x%. The expected response would be 1 million * x%.
- The cost of the marketing channel to reach out to a customer: This will help in calculating the cost of executing the campaign.
- It is good to have the funnel conversion rate of users landing on the site to submitting a lead/conversion. Over a long period of time, it can be fine-tuned for each persona. This enables marketers to calculate potential revenue by executing a campaign for each persona.
Let’s call this as a segment or a persona matrix. Once the segment or persona matrix is ready marketers can decide where they should spend media budget depending upon the marketing message and channel thereby enabling them in prioritizing spends.
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Gaurav is a part of the AIM Writers Programme. He is a data consultant with over 12 years of experience working with digital marketing data. His passion is to work with large data sets generated by various customer touchpoints, assimilate and generate meaningful insights. He usually likes to use these insights and act upon them to optimise user journey and personalise customer experiences. Gaurav is also an Adobe certified expert in Analytics & Audience Manager.