What is customer insight?
Customer insight could, of course, mean anything. With that in mind, David asked Christine to break down the different types of customer insight.
“First of all, it’s important to think about what types of data we’re collecting in order to generate customer insight,” Christine said. “So, I identified five sources of data: competitors; customers; markets; employees; and channel partners. And out of those five data sources, we’re collecting four different types of insights: market predictions; customer segments; propensity models; and customer analytics.”
Market predictions
When probed on ‘market prediction’ insight, Christine said: “This has traditionally been the domain of market research, but now what we’ve got is actual customer data that can not only help us to make predictions about the total market size and the current market share, but also about their sales potential and the trends and issues likely to affect the company’s ability to achieve this potential.
“So, customer insight can now drive strategic decisions about which markets to operate in and what products to develop, rather than just designing great products and deciding whom to sell to.”
Although it still can work both ways (i.e. you can of course still design a great product, and then decide whom to sell to), there is now the technology available that helps you gain insight into what customers want, and then use that as your starting point.
Customer segments
When it comes to customer segmentation strategy, this depends on the type of business, consumer base, and more. For instance, in B2C, you’re probably segmenting according to demographics like affluence, life stage, geography, and so on. In B2B, people segment according to value, size of company, sector, etc.
However, regardless of whether it’s B2B or B2C, Christine hammers home the point that segmentation has evolved hugely because of the level of customer insight we can now acquire. “The days of spray and pray are truly gone,” says Christine. Now, companies are using digital footprint data, psychological profiling, intent data, and more. With this in mind, it is now possible to gain really deep insight into the wants and needs of customers.
Christine added: “In the book, we talk about a five-step process to do that segmentation. We also talk about the importance of ABM, which used to be something only used for high-value enterprise, but there’s no reason not to use it for any company size now.”
Propensity models
“This is all about how you predict the behaviour of your customer base,” says Christine. “For instance, the likelihood of your customers to respond to an offer or make a purchase, or the likelihood to churn. Propensity models can also be used to build profiles of typical customers, or to identify and target other potential customers with a similar profile.
“They can be used on their own, or they can be used in conjunction with other things. So, for example, a mobile phone company can predict which customers are likely to churn, but are not going to respond to an upgrade offer.
They can also be used to predict next best action or NBA. NBA takes into account customers’ past actions, history, behaviour, interests, challenges and needs, blends this together with external sources of insight and AI predictions, and identifies the next step an organisation should take which will both align with its marketing goals and meet the needs of the customer at that moment in time.
Customer analytics
Often, marketers are so heavily focused on acquiring new customers that they don’t spend as much time as necessary on retaining the existing ones.
Christine, however, argues that we can use customer analytics to effectively kill two birds with one stone. By using customer analytics, we can understand our existing customers, therefore helping to retain and develop them, but simultaneously giving us a mould of what other prospective customers might look like.
Therefore, using customer analytics can help marketers to both retain and acquire.
When and where to engage
Christine went on to say that only between 2 and 5% of B2B audiences are ready to buy at a given time, and yet we usually market to 100% of the customer base all of the time. In addition to this, we also know that around two-thirds of the buying journey now happens online.
This means we need to understand the buying journey and establish who this 2-5% are, in order to know when to engage, and whom to engage with.
That’s why things like intent data and digital footprint data have become so important. Behavioural data has now also become increasingly important, as this can show us surges in intent from certain sectors or companies. For instance, have three senior people from a given company searched your solution? Maybe it’s time to engage with them.
Start with the goal first, and then select the technology
David and Chris then spoke about the choice of technology in a customer insight strategy. In other words, what tech do you need and what don’t you need?
Chris started by referring to Scott Brinker’s martech landscape graphic. This graphic shows that there are now 1200 technologies in the ‘data’ category alone (one of six categories overall). This category is so extensive that it’s been broken down into 10 sub-categories.
So, quite a lot to digest. With such an ocean of technology available, we could be forgiven for being overwhelmed. Chris, however, says the solution is quite simple: start by asking exactly what you are trying to achieve with your customer insights strategy. Are you looking for insight to retain customers? Are you looking for insight to acquire customers? Once you know exactly what you are looking to achieve, you can then look for the technology that fits your needs. Absolutely don’t, Chris argues, get the tech first and then think what you can do with it.
Chris also mentioned that The Martech Alliance has a ‘four Ps’ methodology, which helps you to select the right technology depending on what you’re trying to achieve.
How insight can be at the heart of all campaigns
Next, Chris discussed the importance of moving away from ‘spray and pray’ marketing to ‘next best action’ marketing. In other words, rather than creating a campaign and asking ‘who can I target?’, you’re taking each customer or prospect separately and saying ‘They’ve taken this step in the journey, so what’s next?’ This approach is customer insight driven as opposed to driven solely by a predestined marketing plan.
In effect, this means implementing ABM – whether that’s one-to-one, one-to-few or one-to-many. So, with this in mind, Chris offered a five-step approach to implementing a customer insight driven ABM plan.
- Research your segments. Make sure you’re clear on who your segments are, and what they need. What is your ideal customer profile (ICP)?
- Bring your segments to life using personas. Identify individuals in those accounts to make it ‘real’.
- Select your accounts. You can used defined criteria, or you can use martech to help select your accounts. For instance, AI can help generate ‘lookalike’ accounts. Customer analytics comes in useful here, as it means you can give AI the profile of an existing account, and instruct it to find other similar accounts.
- Find out how those accounts are behaving using account data. Who’s in market? Who’s consuming your content? Who’s engaged? Who’s surging or trending? Are there multiple people from an account you’re targeting looking at the same content? Look for early buying signals. Are there certain companies or industries that keep cropping up?
- Target your accounts with relevant content.
