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The primary objective of the marketing department in any organization is to facilitate great customer experience and ultimately make it the biggest differentiation from the competition. If you look closely, there has been an increase in the number of customer experience software and platforms but the real improvement hasn’t really improved a great deal over the last two years.
Are we looking at the right data?>
Marketers have increasingly become data-driven but for a long-time, the approach to data has been passive, i.e., we always looked at past data or siloed data within a specific system. But today’s marketers and salespeople are harnessing technology advancements such as distributed data architecture, in-memory processing, machine learning, artificial intelligence and so on to unearth insights in real-time, which was unimaginable few years ago.
However, the key question is – are we looking at the right data?
Now, this question has two parts:
- Is the data up-to-date and valid?
- Are we even considering the right data points?
Consider this – A vast majority of tech platforms are able to collect and analyze structured data, which according to research data from Oracle amounts to only 12% of the enterprise data. So, even if our intent is to collect and understand customer feedback, we are looking at only partial information.
A lot of information is usually in hidden in what is usually referred to as “dark data” or in simple terms unstructured data. In fact, dark analytics is emerging primarily to look at information that is not structured but available across various sources such as text messages, social media, forums, job sites, email, video and more.
In short – it is untapped data in your possession, untapped data available in the deep web and non-traditionally available data.
Let’s take an example – let’s say, a hotel wants to improve their overall customer experience and the information they are looking at is the feedback given by the customers about the quality of food, taste, service, etc. Is that information enough? What if the primary reason for the decrease in footfall is limited parking facilities?
So, it takes us back the question above – are we looking at the right data?
Need to validate data
Alright, so let’s address the first question – is my data valid? It is obvious that when data analytics doesn’t yield the expected returns, the immediate factor to look at is the data itself. Data can become irrelevant over a period of time due to factors such as data decay, thus making existing data unusable. For example, if I have information about a prospect in my target company, and if that information is outdated or inaccurate, then my marketing messages will be sent to the wrong people, it can lead to misclassified prospect segmentation and reach out tactics.
So, the first step is to have a holistic approach to enrich data and maintain data integrity. At Fiind, our AI-driven sensors constantly look for updates in data or events, thereby activating a bot that knows where to look across the web to get the maximum value.
So, as a result, what starts as an email or domain name transforms into an enriched organizational profile, with not only actionable firmographic information but also customer signals on buying intent and product fit to identify your total addressable market and serviceable market.
Check out the whitepaper on Data Stewardship to understand this better.
Are we considering the right data points?
It is not about how much information are we having about our prospects and customers, but about having the right data points. We need to make sure that we are considering the right and relevant data points to measure. This is exactly where a tool such as Fiind Smart Signals, comes into play. It not only picks up the relevant information across sources but also segments your entire customer and prospect database into segments based on the signals exhibited.
For example – if you are selling a CRM software, even signals such as your target organization hiring a lot of salespeople, if they got funded recently and data points about their tech stack to identify your product fit are all relevant.
So, to offer great CX, being data-driven isn’t enough. You need to go beyond structured data and demystify the dark data that can give you relevant information. Today’s AI for sales and marketing help you identify the right customer data and offer the sales intelligence to build relevant conversations.