Some of the most successful companies of all time have achieved that success by effectively collecting, analysing, and leveraging customer data. This simple truth has been repeatedly confirmed by research. For example, Gallup’s investigations into behavioural economics have found that organisations that fully leverage insights into customer behaviours generally outperform their peers by up to 85% in terms of sales growth and around 25% in gross margin (Behavioural Economics; Gallup.com).
Of course, one doesn’t need external research to confirm that effective use of customer data is a cornerstone of customer acquisition and steady sales and revenue growth. You just have to look at the stellar growth of some of the giants of modern industry like Google, Netflix, Facebook, and Amazon for clear evidence of this fact.
These are all companies built on the digital sales and delivery of products and services, which is not the case for the majority of companies. But while the Googles and Facebooks of the world may have an easier time of collecting customer data, doing so is no less important for the sustainable survival and growth of, for example, a small hardware store or a national chain of fashion retailers.
One of the few positive consequences of Covid-19 and the lockdown response is that the majority of businesses are now acutely aware of the imperative to digitise their operations, and the value that can be unlocked by collecting and using customer data effectively. However, therein lies the proverbial ‘rub’. It’s one thing to collect data, but another thing entirely to analyse it correctly, extract appropriate insights, and most importantly, harness it to contextually understand exactly what your customers need, exactly when they need it.
When it comes to data analytics, the first prize for most retailers is to gain a better understanding of their customers’ needs, expectations and purchasing patterns and behaviours. The idea being that these insights can then be leveraged to create the best possible customer experiences and, in turn, drive enhanced business outcomes (aka profits). However, in a changing, fast moving, and highly digital world, achieving these objectives using historical data is becoming increasingly difficult and elusive. So steadily growing numbers of businesses are working towards gathering current customer data and acting on it as quickly as possible, even in real time.
Undeniably, the need for, and ability to deliver, this level of real-time customer sales or service depends on the type of business one operates. For example, few, if any consumers need a reminder that they should go to the store to buy groceries. But for the person who has been spending time on search engines clicking on offers for new shoes, a well-timed email from a footwear merchant, possibly including a discount voucher, could be a significant value add.
And collecting data in real time is the easy part, as is analysing it – provided you have the right tools and expertise – but one of the biggest stumbling blocks that most businesses encounter is implementation. That’s true in three areas. Firstly, many businesses struggle to figure out what the consumer activity is that hints at them being prospective customers right now, and then finding ways of gathering information on that consumer activity.
Secondly, many businesses experience stumbling blocks in terms of proactively using the data and analysis to drive greater sales. And finally, the majority of businesses find it difficult to operationalise the customer behaviour triggers they observe. And that’s evident in the high number of businesses that fail to make it easy for their customers to respond to a call to action or an offer sent to them on the basis of their recent shopping or searching behaviours.
The answer to all these requirements is, of course, technology. While it’s not humanly possible to monitor all your existing and prospective customers’ online activities and respond to each of them in a way that prompts a purchase, it is technologically possible – and in fact relatively easy. However, it’s important for any business considering this type of tech-driven real-time customer engagement to carefully consider whether it is the right approach for them, and their customers. And to then go about it in the right way. And that’s where careful analysis of existing or historical customer behaviour data is still key. The truth is, it’s very easy to respond digitally to a person’s online search activities; but it’s even easier to upset them, annoy them, or drive them away if the real-time engagement is seen as intrusive, invasive, or not perfectly matched to their customer experience preferences.
The bottom line is that real-time customer engagement and sales is not for every business. If your organisation doesn’t already have a tried and tested ‘traditional’ marketing strategy in place, attempting to win customers through a real-time approach is probably not for you. And even if it is, you would be well advised to proceed with caution, enlist the services of professionals who know how to do real-time well, and partner with a seasoned data analyst to use the behavioural data you already have to establish the solid engagement foundation, strategy and processes that have the lowest risk, and highest potential to win over the hearts, minds, and wallets of customers.