I have hacked with engineers, worked with marketers, rubbed shoulders with designers. But working with sales teams has been a completely different world for me.
While most other hats require you to work on bits and bytes, sales teams work primarily with people. This makes the work much more challenging and exciting.
A day in the life of a sales rep involves emails, calls, screen-shares, meetings, calendar invites for more meetings, notes, follow ups, prospects, research, due-diligence, wrap-ups, products fits, up-sell & cros-sell, demos, quotes, negotiations, discounts, filling goals, checking quotas, commits, refining forecasts — all for one opportunity.
Now repeat that over and over, every day.
It’s not hard to see that sales automation systems and sales CRM software have become indispensable for effective tracking on sales teams. Every sales organization has one, and every sales professional logs the activity into it (or at least they’re expected to).
Now, logging is a passive activity that involves painstaking effort and discipline on part of the professional, but a data log by itself does not generate any additional value to its creator. Until now, CRMs have mainly served as databases. They don’t do anything to actually help reps perform at a higher level.
Making sales teams productive is about making the data work for them. Data is the new oil, but it’s only useful when it can be used to fuel intelligent algorithms that augment human intelligence — popularly now known as Data Science.
In the past decade, we've seen a growing number of companies embrace big data and adopt data science as part of their product offerings — Google search results, Netflix movie recommendations, Amazon retail recommendations, Paypal fraud detection algorithms, Facebook friend recommendations, Linkedin Job matching and many more. This almost always leads to better user experience, more sales, and higher customer quality and satisfaction.
In the Enterprise space, CRM is often treated as the heart of a company’s data assets, but we as an industry have only gotten started with big data and are yet to realize the full potential of data science.
Data science for Sales in broad strokes is about:
- Collection of various data sources related to CRMs (Big Data)
- Applying advanced data munging (Data Mining) to connect the dots
- Leveraging rigorous science (Machine Learning) to extract advanced insights (Prescriptive Analytics) and build models that predict a future state (Predictive Analytics)
The resulting data products have a single goal — making all the stakeholders in a Sales organization more productive.
Let's look at some of the possibilities that data science can enable for sales organizations.
Data can help organize the next set of opportunities to focus on that may increase chances of meeting his/her quota. Algorithms can also assist in research and communication with a lead/prospect and plan timely followups — that can turn a lost opportunity into a potential one.
Algorithms can help sales reps decide between which product to suggest to the potential customer or what discount percentages would increase likelihood of a sale — and doing in real-time while still on that call.
Data can suggest not only the best opportunities to work on, but the next set of actions to take that improve the conversion rates — akin to a turn-by-turn GPS for sales professionals.
Where is the next 10% improvement in revenue coming from? This is a question that’s on every manager’s mind. Does it involve re-assessing the performance of lead-sources, or re-arranging work loads of the reps? Whatever your strategy is, data can help, with consistency.
Guide your team with data supported insights and not just by gut feel or even worse, none.
Data collected about activity of the representatives at various granularities can bring much needed visibility into their performance and intelligence can then alert managers to take charge at the right moments to save that $$$ deal.
If there is one thing sales executives care more than a healthy sales revenue, it is an accurate forecast of the sales revenue.
Data collected automatically on how deals are progressing in the sales pipeline can be used to forecast revenues in a consistent and less complex manner. Also, unlike humans, robust algorithms are objective.
Data science for your sales team
The technology sales teams use today is primitive and needs to adopt to the growing data needs of sales professionals, and this isn't an option for most sales teams.
The data scientists and engineers at Zendesk are innovating on all aspects of sales productivity and building intelligent products that help you take advantage of big data. To learn more about what we're doing, have a look at Zendesk Sell.