For years sales and marketing teams around the world have failed to understand the importance of having a data scientist in their ranks. There are several reasons for that but perhaps the most significant one is a failure to recognize and embrace the difference between data analytics and data science.
Data science vs. data analytics
We have all heard of the quote – think outside the box – yet only a select few of us can actually truly do that. Consider a hypothetical box of knowledge. Now consider feeding this knowledge box with data. Once data enters this box, you would know data and you would find ways to interpret this data. Even when you don’t know what the interpretation will lead to you are still aware of the data, or datasets, you can use to potentially arrive at a meaningful interpretation. But what happens when you don’t have access to the data itself? What happens when you don’t know what will lead to any meaningful interpretation? What happens when you don’t know what you don’t know? That’s when you think outside the knowledge box.
This ‘thinking outside the knowledge box’ is data science. Everything inside the knowledge box is data analytics.
You can put data analytics to work once you have access to data but for data science to actually be of use you have to keep thinking outside the knowledge box pretty much all the time.
Using data analytics to rev up sales
Data analytics is the modern day equivalent of cold-calling your potential customers to get insights into their likes, dislikes, and buying behavior before finally making your sales pitch. The difference is that data analytics is far more insightful and has a far greater success rate.
Many businesses use data analytics tools to strategize their future course of action. For example, businesses want to bring as much traffic to their websites as possible and to do that they use a combination of strategies such as email marketing, social media, tools like Google AdWords et cetera. But not all of these strategies are equally relevant or successful for all kinds of businesses.
While a concerted effort in strategizing is welcome, it is far more beneficial to identify which channel is bringing the most amount of traffic to the website. If you can identify this then you can strategize accordingly and maybe decide to put more time and effort on channels that are yielding better results. This is where data analytics comes in. You may continue to use AdWords but if you had evidence that social media is fetching you far more traffic and in turn helping you sell more then there is no reason why you wouldn’t want to amp up your social media strategy.
Identifying the right channels using data analytics is a far more important process than it gets credit for. According to a study by The Economist Intelligence Unit (EUI) and marketing firm ZS, a very small percentage of companies that consider data analytics an integral part of their sales and marketing efforts actually ‘get it right’.
Merely incorporating data analytics into your business because everyone else is doing it doesn’t help. Using data analytics to identify the right channels for lead generation, customer acquisition, and customer retention is what helps the bottom line.
Using data science to plan well in advance
Data analytics will give you all you want to understand and execute at an operational level but data science is what gives you actionable insights into the future.
Data scientists use predictive analytics to identify the exact events that cause things to change and how this change affects other things in the short, medium, or long term. Then there is prescriptive analytics which is the closest thing to a modern day Nostradamus that one can hope to find. Using prescriptive analytics one can actually, rather accurately, shape the outcome of future events by determining causation well in advance.
In the not so distant future organizations will be able to use data science to find actionable insights on optimal price points for products. For instance, mysmartprice.com, which is a leading price comparison brand in India, will be able to recommend these optimal price points to online vendors in real time which will help increase demand for products.
History tells us that organizations that adapt to changing market dynamics tend to survive longer and the only way to avoid becoming a Kodak moment (pun intended) is to constantly try and seek what the future may hold. This is why data science is your elixir on steroids. Not only can data science methodologies help you see the future but when done right they help you decide what to do with your future when it finally arrives.
By using data science to plan well in advance you give your sales team the opportunity to not just survive but thrive. When armed with critical information such as which leads have a high likelihood of closing, sales teams can determine which leads to prioritize. Being able to do that has a huge impact on revenue as time wasted on leads that may never convert is minimized and time spent on leads that do convert is maximized.
Accurate demand forecasting also becomes a possibility when data science is put to work. Imagine being able to determine the demand for your products or services well in advance and not just meeting customer expectations but exceeding them. Knowing how to increase a movie’s box office revenue can prove to be a game-changer for distributors and the movie industry is getting increasingly better at it now thanks to data science.
Context is king
A major reason why several organizations fail at successfully incorporating data science and data analytics into their business is the lack of context to customer needs.
Often there is so much focus on sales that everything else takes a back seat. Legendary management consultant and author Peter Drucker once quipped – the purpose of a business is to create and keep a customer. It is hard to argue with that.
If customer acquisition and customer retention are the two primary functions of a business it becomes necessary to identify the context in which these can be realized. As a business owner, one needs to know what customers want now, how they want it, why they want it and what they may want in future. This is why having context to customer requirements helps a great deal. Both data analytics and data science help businesses understand the context better.