As someone from the tech background, I’m often inclined to think about the “how” than the “why”. A few years ago, when I was working for a different company, we had built a custom product for a customer and we stopped at the MVP stage to get customer feedback only to realize that we were synonymous to the image below.
The developers were just coding to the specs given to them and all user stories were built exactly as described. Luckily, we were just at the MVP and thanks to the scrum meetings, we could recover. That day, we learned something the hard way – it is not just about what the customer says but about understanding their behavior, culture, and purpose – so that you add value. And that something helped us transcend from being transactional to become transformational.
Access to the right data and reason precedes data analytics
At Fiind, we are focused on empowering B2B marketers and sales professionals with our AI platform. While talking to marketers, three words we hear most frequently are “acquisition”, “retention”, “brand-love”. Though marketers are looking at the above-mentioned metrics, the key question is – what do these metrics actually mean? More often than not, we tend to think of “how” to solve a problem but the key is to understand “what is the problem and why is it a problem”. The answers may as well lie there.
Now, if you ask what is an acquisition of a customer, what it takes to retain a customer, what makes people love your brand – that will drive you to ask yourself “what do you know about the customer/prospect?”
AI and machine learning have made life much easier today to get information about customer intent, at the tip of your hands. Tools like Fiind Smart Signals platform, not only identify customer intent and fit but also segment your entire list based on sales propensity and readiness.
That said, it is unlikely for one platform to hold the keys to all the data and the intelligence. This means there is a need for more openness and interoperability across platforms so one platform could harness the strengths of other platforms. An example is such a partnership is between Alexa and Cortana, or how Fiind integrates with CRM, Marketing Automation tools, and other business apps in the user ecosystem. The users benefit by accessing everything through their preferred platform.
More importantly, the quality of data and how it flows through your organization and business applications – sets the context for how successful it can be.
Data flow matters, bad data shatters
The key, however, is to have access to the right data, in the right systems that can be leveraged at the right time, which in turn depends on how data flows across your organization, processes and business applications. While the data flow and the processes may be designed for optimal efficiency, the effectiveness of data analytics almost always depends on the quality of the data. It means, regardless of how good your product is, how creative your marketing campaigns are, it may never be efficient in reaching out to the potential buyers at scale.
Therefore, it is important to acknowledge the cost of bad quality data. Bad quality data not only means bad data analytics, it also means marketing messages are sent to the wrong people, sales professionals waste their time trying to reach prospects who aren’t sales ready. Also, it can lead to misclassified prospect segmentation and reach out tactics.
“What and why” matters. Rest is incidental
To conclude, we often spend a lot of time in the “how” part. It is important to validate whether we are seeking an answer to the right question or if the question really makes sense. Clarity on that will drive us to validate the right data and data sources, thus ensure accuracy in your targeting, segmenting and lead scoring process.
Happy data crunching!