Earlier this year we began looking at the idea of Data Enrichment becoming a core pillar of an organisation’s broader data management strategy. In our first article, ‘Better audience profiling will drive better business outcomes’ we discussed the art and science of data profiling – why it’s vital, how to do it effectively and the improved outcomes businesses could see as a result.
In our latest article, we’re going to discuss how Prioritising and Personalising your engagement with customers can help drive an increase in performance.
Prioritising and personalising
So what do we mean by Prioritise & Personalise?
Prioritising is the ability to analyse customer data and understand where profitable segments lie. This in turn informs decision making regarding which customers to go after proactively, which to sell to reactively if they make an approach themselves, which customers are the most important to hold on to (from a value perspective) and which segments you will need to actively de-prioritise as they are a resource drain on business performance.
Personalising is the process of giving the perception of a tailored solution or engagement even when communicating with many people. The more personalised, the more likely the engagement is to be successful. If we think about the retail sector (mass market with much to gain from personalising communications and engagements at scale) then potential areas of personalisation could cover, how many staff you employ to manage customer care, visual merchandise layout and positioning, store locations and fit-outs etc.
Prioritising personalisation a necessity
Generally, having a succinct way of prioritising anything will help add focus and improve the end result. The same can be said for acquiring new customers. Having a laser-focused plan, supported by the very best third-party data and analysis will put organisations in the best position to succeed.
We’ve already tackled profiling data, appending additional third-party data in order to get a true understanding of clients, and now we need to prioritise who we are going to communicate with, and personalise that outreach to maximise the impact.
By combining Experian third-party data, from tools such as Mosaic, you can start to understand, in great detail, which segments of your customer portfolio are the most attractive for expansion.
In addition, the necessity to personalise communications and experiences goes even deeper. 66% of consumers say that encountering content that isn’t personalised would stop them making a purchase*. And the majority of consumers are overwhelmingly willing to share more of their personal data, if they believe it will help organisations personalise their engagement with them.
Data quality still critical to new business success
If we look specifically at the retail sector again, research has found that a whopping 79% are actively investing in personalisation technology right now*. This is totally understandable given the mass commoditisation of products and services and the need to win customers based on emotional attachment and trust.
And yet, the data that underpins their efforts is not always up to scratch. We’ve looked at numerous retail organisations we work with and analysed the accuracy and completeness of their data assets. On average, only half** (52%) of customer address records held are accurate and nearly one third** (28%) of emails held are not valid. Clearly these inaccuracies would impact any attempt to successfully increase new business sales.
Find new customers today
Many organisations around the world trust Experian to help them understand their target audience, segment and enrich their data, and then personalise their messaging at scale to maximise results. Consumer’s have come to just expect an effective engagement strategy from organisations they interact with. There is simply no excuse for not having an effective engagement strategy in place.
*https://www.forbes.com/sites/blakemorgan/2020/02/18/50-stats-showing-the-power-of-personalization/#6747ffeb2a94
**https://www.experian.com.au/customer-data-validation?chart=Retail