Crossing ‘The GAP’
Before we look to cross ‘The GAP’, let’s revisit the definition of level 4:
Level 4 – is about personalisation and taking a 330° view of a customer (330° because of privacy). You have built models on customer lifetime value (CLV) and are automating either content or marketing. You likely have a customer relationship management system (CRM) integrated with the rest of your marketing business technology stack. You may use a customer data platform (CDP) and an integrated data warehouse. There are various degrees to success but holy shit, you’re doing it – Omni-channel Marketing through Automated Personalisation.
Crossing ‘The GAP’ is difficult because you suddenly need to look beyond the marketing team and often the marketing budget. You will need to work more closely with developers and IT, potentially bring on costly tools and contractors, and get some solid senior stakeholder support. It’s a tall order, but something you can chip away at. Let’s look at examples and case studies of how other companies have taken this leap of faith.
Source: McKTui Consulting
Level 4 – Building a Better Picture – Data Tienda
Let’s look at what can be achieved with a recent Cannes Awards winner. First, if you don’t know of Cannes, it’s an award ceremony often beyond reproach with little room to influence or buy awards.
Millions of low-income women in Mexico cannot become entrepreneurs because they cannot access bank credit. According to the National Banking and Securities Commission, 83% don’t have a ‘credit history’, so their loan applications are rejected. Paradoxically, they are women who have received loans from neighbourhood stores all their lives, so they actually have a long credit history. Data Tienda collects this information from the neighbourhood businesses and uses it to create a digital credit history that secures them with the banks and allows them to obtain microcredits, financial inclusion, and economic autonomy.’
Data Tienda was described as, ‘A powerful example of elevating “invisible data”’. It was brought to the competition by investment firm WeCapital in partnership with DDB México and centred around raising awareness about the challenges women in Mexico face when trying to take out loans that require a credit score. The campaign, ‘Data Tienda’, earned the Grand Prix award; it populated legacy pen-and-paper bookkeeping data from local bodegas into a database that created a credit score 86% of Mexican women didn’t have. This credit score then allowed the bank to give them small business loans.
This is a good reminder when evaluating our data resources (Chapter 5 – The Pillars of Personalisation) to think creatively about what we have that could be used and unique to our organisation and to consider this even if we don’t currently have access to that data or know we could get it.
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In this example, the project used a form of simple AI to collect the data they needed, whereas in the past, collecting the required data would likely have been cost-prohibitive. Rather than rely on a large team of data collectors, they created WhatsApp bots to deliver the form to the nearly 50,000 shopkeepers that had participated as of the time of the case study. And it worked! Over 10,000 new credit records were generated, allowing participants to move forward within modern banking practices.
An excellent place to start is by bringing our existing data together for an immediate problem or application and asking the initial question, should we have this data at all? How can we automate a process that enables our customers to opt in and out freely without resentment, and what can we offer in return for this data?
This is often referred to as the value exchange. With our Cannes example, it’s a win-win for most involved: customers share data to get loans, and those providing the loans get to issue more loans and improve overall credit scores, which ensures a high likelihood that loans won’t default.
It can be as simple as improving a customer’s experience with the brand by not advertising ‘at them’ with messages that are not relevant – as long as the data you’re asking for is not too personal or time-consuming for the customer to provide.
