Big Data

Data Sales

Why Sales People Should Think Like Data Scientists

How important is data to those in sales? Should it be the starting point for all sales?

With 2017 fast drawing to an end, the importance of data has never been so clear. The digital age has given birth to both opportunities and challenges, and if you are in sales, you are probably in the midst of this transformation.

The internet has transformed the world into an interconnected entity, with trade largely defying borders and politics. At the same time, the shift to digital-only and hybrid ‘phygital’ business models gave rise to an unprecedented amount of data generated by users daily while they interact with brand’s websites, apps, connected devices and support centers. With the half of the world’s population being active internet users and 1.61 billion of them as ecommerce users (WeAreSocial), it’s not hard to imagine the exponential pace of big data growth.

The major challenge of those salespeople selling to digital users today is, in this regard, something of a paradox. Increase in connectivity has boosted the amount of data to such an extent that it is often difficult for salespeople to sift through the chaff in order to find useful data that could help them sell better. A study carried out by ESRI UK and cited by Forbes found that 35% of respondents were so overwhelmed by the “information overload” that they reported no longer being able to cope with it.

Data is where it all begins

Yet data still matters, particularly in Sales and Marketing. These functions are at the core of every business. The typical business concern is geared towards reaching out to new customers, generating leads and converting them. Big data serves as the starting point in this regard. With a timely access to the right kind of metrics, businesses can shape their strategies based on a careful analysis of market trends.

Digital sales channels have been gaining in prominence over the past few years. Data from the Pew Research Center showed that 8 in 10 Americans now do their shopping online, with 51% making purchases using smartphones.

Such data allows businesses to make far reaching strategic choices. In the example above, it is obvious that with the world moving towards the overwhelming adoption of online shopping, the internet is where all brands wishing to make it in today’s cutthroat market need to be.

Data clearly matters in this regard. Indeed, it is safe to say that data is where it all begins. It inspires sales and marketing innovation and allows businesses to find and exploit otherwise hidden niches.

However, it often takes data science, not sales or marketing, to make sense of the data at hand. The vacancy of data scientist is now one of the hottest in the world – as of September, 2017, there are over 11,500 jobs open in the USA alone. But is a data scientist really that indispensable when it comes to deriving fundamental insights out of available data for sales purposes? Luckily, there’s an opportunity to make do without hiring a data scientist with a 6-digit salary – as long as salespeople on the team can adopt a true data scientist’s mindset.

Ongoing Data Collection

For most people, data collection tends to be a one-off process that is soon forgotten following the accomplishment of a particular set of goals. Data scientists, on the other hand, know that data collection is an ongoing quest that aims to find the most useful insights. That is the major reason why all salespeople should begin thinking like data scientists.

In addition, one of the things that characterize the world today is its high mobility and volatility. Technological advances have not only shortened geographic distances, but also have made it difficult to retain people’s attention. A product that may be all the rage today will soon be forgotten as tastes change at a breathtaking rate.

That is why it is important for salespeople to realize that collecting market data should be an ongoing process to make sure that products and services on offer are kept in sync with market demands. While it is difficult to capture people’s attention, it is quite easy to lose it in the blink of an eye.

Data Measurement Progress

The importance of data is not limited to the identification of market opportunities. What you do after launching your products or services often has an equally important impact on the trajectory of your business. The next step in the process of beginning to think along the lines of a data scientist involves using the metrics that are collected on an ongoing basis as a measure of your progress.

As an example, how many units have you sold in a given timeframe, and how does this differ from previous periods? How many leads have you generated and how many of these have been converted? Having this information allows you to identify those marketing strategies that were successful and those that, perhaps, need to be modified to bring them in sync with your goals.

Thinking like a Data Scientist: Know What to Keep and What to Discard

For almost every business, access to good sources of data is of paramount importance. The digital age is largely data driven, and salespeople have access to huge quantities of potentially useful metrics. The mark of a true data scientist, however, involves knowing what to keep and what to discard. Yes, you have gathered all those figures and graphs, but is all this data useful for what you have in mind?

Sifting through the mountains of data that is available to the average business person is where most people stumble. Nevertheless, it is one of the most important tasks on which you will ever embark. Data in its raw form is an overwhelming jumble of figures. What you need to do is develop a mindset that will enable you to turn this into true business intelligence.

Data Should Be Timely

The importance of timely gathering and exploiting data can never be overestimated. For those in sales, the ability to act timely may be all that stands between a lost lead and a conversion.

For example, digital marketers understand that lead response time is an important factor in determining success or failure of a particular marketing strategy. Bob Schultek, a business development professional, notes that 80% of all leads are wasted largely due to the fact that the sales team either never follows them up or is late in doing so. Indeed, data shows that B2B leads, in particular, need to be responded to within 20 minutes to boost the chances of turning them into customers.

How to Make Data Work in Sales

Without doubt, data matters in sales. In fact, it lies beneath everything related to the customer: from building up digital experience, to finding the right words to ignite their interest, to providing after-sale customer support. Yet, it takes a few reasonable steps to make data work for your sales representatives and managers –

Pick the right metrics for monitoring, even if they seem to overlap with the ones of your marketing buddies (why not work in sync?). With each generated lead, it helps to know their browsing history, behavior and depth of engagement to reach out in the way that really hits the mark.

Keep your eyes open at all times. Ongoing market research in your product or service category helps to identify threats as well opportunities to tap. How can you make sure your unique selling points are on point (pun intended), if you don’t look around to understand what has changed?

Don’t let your data get sour. If data insights are not actionable, it’s a waste of your team’s time and efforts. Data for data’s sake is not far-sighted, so make the implementation stage a part of your ongoing improvement routine.

Build your toolset for data analysis. Luckily, the tech market has something to offer for those who are not data scientists by profession yet want to get their hands on data analysis quick and easy. In-built data analytics in CRM systems and custom analytical modules attached to your enterprise systems are just a few options to get you started.

To top it all, it’s essential to create sales analytical routines that are consistent and sustainable throughout customer lifecycle. By collecting sufficient volumes of relevant historical data and spotting patterns, eventually it will be possible to introduce the most business-critical analytical types of all – predictive and prescriptive ones.

Maria Marinina applies this data science approach as Marketing Manager at the software development company Itransition, With years of marketing experience driving business growth through increasing brand awareness and lead generation.