In our next installment of the Customer-Centric Data Series, we spoke to Stephen Welch, Managing Director, and Ian Bobbit, Chief Analytics Officer from Realise about how businesses can become data-centric companies. Realise, a part of the larger Unlimited Group, helps brands make data-driven decisions to maximize growth.
They discussed how organizations can structure themselves better to become data-driven, what teams, structure, tech stack and KPI’s you need to look out for, as well as some details on personalization and customer lifetime value.
What are the main challenges facing companies that want to become data-centric?
The first thing both Ian and Stephen stress is that most companies already have a large amount of data, but that data can sometimes be siloed by teams. The first challenge is being able to consolidate that information and understand its potential.
Another challenging factor is needing the buy-in from key stakeholders. There needs to be a senior leader in the team who wants to learn from data. Having someone on board, who can manage across multiple teams, will help companies identify data that describes their customers and behavior on a day-to-day basis.
So if we know that the data is already there, the other challenge is to ensure it is being used to reach the correct conclusions. Often companies can have a strategy that is not evidence-based. Becoming data-centric is about being able to recognize effective KPI’s and data about your consumer behavior.
“We try and understand who your customers are and how they interact with your business. Therefore we’ll be quite focused on the customer touch points that’s a really an area that gets us for us,” says Stephen.
Transforming a company to becoming data-centric
Being able to transform a company to be truly data led is not an easy process. Key stakeholders need to be involved and teams need to be able to speak to one another. Ian and Stephen both identified conflicting team goals as one of the reasons companies are not as effective as they could be.
“What we’re really trying to do by becoming more data-centric is provide rich and broader information such as context around the customer that shows needs and behaviors,” says Stephen “We also look at how to engage the business beyond just one specific channel.”
The initial stage often begins with a data-rich area used to prove the effectiveness of change in one specific area and get buy-in from the larger company stakeholders. Next, is to ask business leaders questions like what do they want to achieve? Where do they want to get to? Where are they currently? All these factors help identify what data the company should be looking at to build its data maturity curve.
The search for personalization
We know from our customers at AB Tasty that personalization is one of the most sought-after features for CX. The way to achieve that is through data. One of the reasons it is so difficult to get right, according to Realise, is that the idea of “personalization” means different things to different people. Ian points out that once you start to personalize, you need to have the resources to create content for each different segment and this, in turn, can lead to some very complicated workflows and messaging.
“It requires an awful lot of data, thinking and planning because once you’ve started automated personalized columns, it becomes quite complicated quite quickly,” says Stephen.
Both Ian and Stephen are excited about the new technology appearing on the market to support this, but urge caution as to whether this actually improves companies bottom line, efficiency, as well as overall CX.
Customer Lifetime Value
What they do value as a metric is CLV, complementary to looking at your data in a holistic way. As we approach more difficult times for companies, being able to concentrate on giving your brand value is really important. Ian and Stephen are enthusiastic about brands that are less focused on transactional value with their marketing.
Stephen spoke about a brand that looked at the metrics of their mailing over a year to calculate the incremental increase, rather than looking at the transactional value of each one: “Whatever you’re looking at, if you don’t look at that longer-term affinity and engagement for future value, you’re missing a trick.”
Customer Loyalty Schemes are also part of CLV and Realise works hard to help companies improve them. Part of this is being able to understand who your customers are and what value they are looking for from your brand, in addition to identifying the target metrics you hope to achieve through loyalty schemes Measuring loyalty can be difficult and the cost of running such schemes is often expensive. Companies need to create a business case for it, with clear expectations and markers of success.
The KPI’s for a Data-Centric Company
No two businesses are the same, but we pressed Stephen and Ian to give us an idea of what KPI’s they look for. It is important to see which reports teams are accessing and what metrics they use on a day-to-day basis. To know for certain that companies are looking at future growth, measuring acquisition, churn and NPS is key.
Engagement is also a crucial metric for parsing Customer Lifetime Value. Stephen adds that Data-centric companies should also look at their spend. Sometimes they look at the profit of a particular action, but don’t actually benchmark to see if they could have achieved more.
Each company can be different, but you can approach CLV with a different focus each time – your company (how much profit it is making), your customer (how they are behaving) and your staff (do they have the right tools to help them make decisions).
You can find out more about our Customer Centric led by looking at our previous installment on How To Measure Your Digital Impact.