Digital Alchemy | Turning Data into Valuable Customer Insight
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Turning Data into Valuable Customer Insight

Customer experience is no longer a buzzword – it is a necessity. How your customers interact and journey through your brand’s multitude of touch points will determine whether or not they will become loyal. With so many analytics software and tools, organisations have so much access to a large pool of customer data – but are unable to utilise it in the way that yields the best value to their business.

Lack Of Direction In Data Collection And Analysis
Today, many businesses are overwhelmed by zillions of customer data and often don’t know what to do with it. They frequently fail to utilise customer data because their data strategy is not tied with the business problems they are trying to solve. To get the first step right, the real questions they need to be asking are: What is the business problem? Why does this happen? How to solve it? With well-articulated objectives, businesses can define data they need to collect and analytical framework they need to use.

The Problem with Customer Data
The value of customer data can be fully realised only if it has been handled by proper analytics that generates hidden insights and useful information to develop suitable marketing strategies. The biggest challenge isn’t so much about gathering new data, but rather analysing and correlating the data that’s already in place, to make sharper business decisions to retain and build loyal customers. The problem is that a lot of businesses don’t know how to handle this data and are using it blindly. They create generic reports of stats and figures but are inclined not to make insightful connections. Instead we must learn how to use this existing data in order to make predictions about customer’s behaviour and craft future marketing campaigns and communication messages around that.

“IT’S NOT ABOUT THE SIZE OF THE DATA, IT’S WHAT YOU DO WITH IT”

Building Seamless Interactions
Large customer data (interactions, demographics, purchasing patterns, etc.) with insightful analytics can build seamless customer interactions – be it in financial, retail, banking or the telecommunication sector.

Not all your big data is useful so it is important to start with customer data that solves their pain points. Brands must look across multiple facets of consumer data, which will allow them to better understand customer preferences and interactions with your business. Let insights from this process guide you through your customer data, not the other way around. This will allow you to create personalised experiences for customers at every touch point – from the point they visit your website, to checkout at your shopfront, to the customer service department and feedback via emails or phone calls. Connecting with consumers on a personal level is essential across every industry.

Basically, this means creating multiple “customer-centric view” based on existing data that focuses on “what do they love, what are they rejecting, what influences them to buy more, and which customers are the most loyal”.

Don’t Create Silos for your Data, Integrate Them
In a typical day, you may have 100s to 1000s of interactions with customers, whether it is online, mobile, your store or even through direct phone calls. Data on each is great to have, but what is more useful is when you bring them all together as introduced in How to Deal with (big) Data. It can create some useful insights into your brand experience – think when someone searches for your product or service online and then later buys it through a mobile, your store or a kiosk. You need to be able to acquire this data and where there are gaps to better improve the customer journey process.

Connect Transaction Data And Customer Preference By Constant Interaction
Suppose you blast out consistent SMS messages to your database of customers about a loyalty program or promotion and it doesn’t convert and you don’t know why. That’s a sign that you are not optimising your customer data. You might argue that you build a campaign based on that particular data. But the problem is, if you only rely on one dimension of data such as transaction data and leave out collecting customer’s needs and preferences, there is a high chance that you will send a wrong message, at the wrong time, and also through the wrong channel. Why? Because each customer interacts differently in a particular situation and environmental context. So if you don’t keep interacting with them then, it’s likely that your decision will be based on historical data and it’s no longer relevant to the customer’s needs.

Final Thoughts
Much like your data, your customers are dynamic. They have dynamic experiences, so it is vital to make correlations between where and how customers interact with you so that you can truly understand their mindset, pain points, and behaviour.

The value of customer data depends on how you use your data to make smarter decisions that create more value for your customers. Are you sending impersonal and irrelevant offers? Did you send a promotion at the wrong time or because you didn’t look deep enough at your customer’s behaviour and preferences? It’s time to start analysing all that data better to create improved customer experience – otherwise what is the point of it all?