Thought Leadership

Why big data for customer experience needs to get some context

As consumers, we live in a digital ecosystem almost without even realising it. We interact with brand touchpoints many times a day, and in each of these our brand perception is shifting and evolving. More than this, rather than buying products from companies, we buy into their brand. Customers for any product or service are now expecting the personalised touch, and a tailor-made customer experience to be delivered through their screens.

The explosion of big data has therefore presented businesses with the opportunity to rethink and reshape their brand attitude and tone. However, it has also posed a more pragmatic challenge of how to maximise the potential hidden in the huge data sets they have been building over years. This is where customer analytics comes into play. Analytics can turn the massive amounts of data you already have on your customers into insights. Keep in mind that this is something different than traditional analytics or business intelligence.

Whereas traditional analytics look to the past to predict trends, customer analytics look for what a customer will do as an individual.

The ability to leverage and maximise the value of this treasure trove is now more mission-critical than ever. Building relationships with customers now involves more frequent, but far shorter interactions. Yet it is on this the brands rely on to build the lifeblood of any company – brand loyalty. Customers have more access points than ever to their service provider. For instance, they can interact on the company’s website or app, over the phone, or on their social media accounts. These kinds of interactions take less time than an in-person visit, but they happen much more frequently. They are also not as profound like face-to-face interactions, making it even more important to have each one count.

Companies need to take advantage of all of these touch points and interaction moments to provide real value. The only way to do this is to be relevant in ALL areas of messaging, timing, and to ensure these are relevant to the customers’ context.

But how does one become relevant? The first step is understanding who your customers are.

Let’s put it in Context

Customer data analytics are powerful. Companies today have access to layers upon layers of information about their customers, from location and age to spending habits, to attrition tendencies, to product and communications preferences, to behavioural intelligence.

Consider a recent report from IDC which predicts that data creation will reach a total of 163 zettabytes by the year 2025, this is a ten-fold increase in worldwide data. Companies have so much data at their fingertips, but unless that information is channelised and used effectively, it’s useless.

Contextual relevance is the most important factor in extracting value from data. Understanding human behaviour, but not being able to apply it to common-day contexts, such as where someone is or what were their previous actions in the last hour, will only allow a company to better understand what has happened in the past. There is little value in living in the past when a business’s value is counting on what happens in the now. This is especially true when considering a customer’s experience. If the content ignores a customer’s context, the brand’s marketing efforts will be futile, the needs of their customers will go unmet and customer relationships will diminish significantly.

Real-time reactions

So how does a company use their data to deliver relevance? The answer boils down to having actionable insights available at your fingertips. In order to leverage knowledge on the customer context, companies must move to a process which combines long-term historical insights with up-to-the-minute processing of real-time behavioural data.  Companies must then use the enormous amount of existing user data to constantly create connected user experiences; as compared to those created by technology companies that are dependent on web traffic activity and information alone. Productively utilising customer data allows a company to determine what a customer is most interested in, and to create a personalised experience where content, products and/or services are presented to customers before they even realise their needs. As customers’ expectations of their favourite brands increase, they have more of an affinity for those that offer more pertinent information, instruction and added convenience to their lives.

Marketers must be able to truly manage the conversation with the customer, both in- and outbound. Companies can do this by using their data to deliver the most relevant, timely and contextually-aware actions that match the needs of each and every individual customer. When a company or brand can execute on the insights gleaned, they’ll become transformative in the way they approach marketing.

Taking the customer journey full circle

By leveraging the context found in data, brands can provide customers with an optimised experience that is more relevant and consistent across all channels. Personalised service can be adapted to their present needs and interests, and tied to the complete customer context – their location, most recent purchases, complaints, etc.

For a company, the value of processing customer data with analytics software can be about optimum marketing results, with more precise targeting, more connected experiences and increased campaign efficiency. This gives companies the ability to acquire the right customers, provide them with excellent service and products, and be more focused on which customers to retain, at what cost. The value of customer data is beneficial for both the customer and the service provider as they truly begin to operationalise insights on customer data and behaviour.

But crucially, data in itself isn’t smart – it takes a level of processing to develop the insights that drive businesses forward. Companies can only utilise data to its full potential by harnessing and harmonising the assets at their disposal, unifying multiple entry formats, systems, and processes. Only once this has been achieved can the power of AI-powered programmes and platforms drive actionable insights to boost customer engagement. Creating end-to-end systems such as these mimics the customer journey in itself, turning dumb data into true connections between business and customer.

Finding it hard to use your customer data effectively?
Reach out and we will guide you.