So crucial are milestones such as these to the financial services sector that marketers can often use them to anticipate when someone is ready to purchase – though it’s easier said than done when much of the decision-making takes place offline and at a household, rather than individual, level. While it’s easy to see when a customer is about to buy car insurance based on their transaction last year, you don’t always know they are moving house until they have browsed for mortgage products.
Where data and insights around major life events are lacking, there’s a danger that digital advertising will lack relevance, and therefore fail to drive up conversions and keep down the cost-per-acquisition. Clearly, it’s impossible to put a customer-centric business strategy into practice if you can’t track the triggers that drive a purchase or application.
Identifying intent, and delivering better customer experiences, can be more straightforward in sectors such as entertainment and travel. The sites someone visits when planning to watch a gig in another city, for example, tend to be closely linked to the activity (i.e. ticketing and accommodation websites).
Yet purchasing cues can be trickier to spot in the financial services sector: how do you know someone has experienced a change in personal circumstances that kickstarts their research into loans or credit cards?
This is why marketers increasingly want to get to know their prospects before they become customers by understanding their online behavior and how this aligns with changes in lifestyle. It might be that someone arrives at a homeware retailer’s site having previously visited ones about weddings, lifestyle and cooking. While not directly related to homeware, they do indicate that this person could be about to move and/or renovate.
Understanding what customers are likely to do next, and the chances of conversion, can be achieved by combining web activity, onsite behaviours (e.g. what products they browsed) and Experian data such as location, lifestyle, household make-up and financial situation.
The latter is particularly valuable to those in financial services, since it not only shows whether someone is in the market for a loan but whether they can afford it. Adding in the composition of the household can be invaluable: if you know one person in the household is looking for mortgages, there’s a good chance their partner will be too. It means you can move away from the usual digital triggers – such as past transactions, log-ins and online browsing behavior – to build up a complete picture of your audience.
To give you an example, we recently worked with a loan provider, looking to optimise its advertising to drive up loan acceptance. This involved the following steps:
- Defining a target audience using client and Experian data.
- Running the campaign and gathering performance behaviour.
- Optimising the process, continually refining the audience based on conversion.
By generating predictive models, refreshed every 24 hours, the marketing team could identify the audiences most likely to apply, and prioritise them accordingly. Significantly, optimised audiences were three times more likely to convert than the original audiences.
Developments in our data modelling mean that financial services businesses will soon be even better informed about their prospects, specifically, the level of risk they pose. Such insights will undoubtedly prove crucial in promoting responsible lending, through identifying whether someone is experiencing financial problems early on and, of course, ensuring digital advertising reaches those prospects who lenders know match their acceptance criteria.
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