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For the longest time, sales has been a numbers game - the more people you reach out to, the more conversations you will have; and the customers you bring in. However, as digitization has set in, sales teams today have tools and resources that they never had before, and this is changing the way businesses do sales today.
The devil is in the data today. Analyzing and interpreting the data from your marketing campaigns help you with learning the elements that drive purchase decisions. And this, in return, provides businesses with data-driven insights that help them execute sales campaigns with high precision.
One of the key elements of modern marketing campaigns is creating touchpoints that allow prospective buyers to interact with business assets. This could blog content, webinars, sales decks, videos, podcasts, to mention a few.
These touchpoints create thousands of data points that can now be analyzed to help with predicting how any particular cohort of customers would behave.
For instance, visitors to your blog page that talks about fundamental topics related to your industry could be from prospective customers at the top of your funnel. However, visitors to your pricing page could be bottom of the funnel who need to be treated differently.
Data science can help unravel a lot more micro-details related to your visitor based on their location, search queries, pages visited, browser or device used, interactions made, and so on. With machine learning, this information could help with predictive analytics on what kind of behavior you would expect with such a visitor and thus apply a personalized sales approach to convert such a prospect.
Sales processes in most organizations involve a large number of internal and external stakeholders. Not surprisingly then, sales can indeed be highly ineffective in terms of output relative to input.
Data science helps sales teams optimize their process by identifying inefficiencies and overcoming them. Here are some ways to do this.
Every customer is unique, and there is no doubt about it. But ultimately, each prospective customer can be defined with predefined characteristics that may include demographics, budget, use-cases, to mention a few.
While it is possible to generate ICPs manually, data science can help automate this process. With data analytics, it is possible to map seemingly unrelated elements that define a customer into one predefined bucket automatically.
The success of any sales campaign is measured through its outputs like the number of customers acquired, or the average sale price. However, a major factor influencing this figure is the quality of leads generation and how they are qualified.
Data science can help refine your business’ lead generation process by identifying what strategies work, and what doesn’t. This also applies to post-sales onboarding and training. Data science can help identify specific strategies that help meet your sales objectives, including retention.
This way, businesses can allocate higher budgets to strategies that work, and bring down investments in channels that don’t work. For instance, businesses can spend more in online courses to train their new customers and perhaps reduce investments in personalized swags.
While this can seem pretty straightforward, data science can go much beyond simply doing the math on return on investment.
With historical data, it is possible to assign a score to each of your leads based on how likely they are to convert. Algorithms can also track thousands of different data points like website visits, email opens, etc. This information can be bucketed with other parameters you know about a particular visitor to offer enhanced segmentation of leads.
With advanced algorithms, it is possible to dynamically score and profile leads to improve qualification over time.
One of the most critical factors that necessitates data science in sales is the need to analyze real-time scenarios and identify optimization opportunities.
This is especially vital in blue chip organizations with thousands of sales professionals targeting a wide range of customer segments. Data science can help track the performance of each of these teams not only against their own historical performance, but also standardize performance of these different teams and track them with a unified metric.
Sales teams must invest in building sophisticated dashboards that offer them real-time visibility into their sales activities, identifying what strategies work and what don’t, improving forecasting, and driving continuous improvement. Modern machine learning based sales optimization tools are vital for enterprise businesses for this reason.
While there is no gainsaying the fact that past data does not always correlate to future performance, data science and analytics can help narrow the gap between the two significantly. There are a number of areas where data analysis tools can help with sales forecasting. This includes:
Churn prediction: identify which of your customers are likely to stop using a product or service
Pricing optimization: Analysis of product pricing among competitors and identifying optimal pricing strategies. This is extremely vital for businesses in the B2C space where minor pricing differences can greatly influence purchasing decisions.
Sales funnel analysis: Identifying frictional elements and areas of improvement in your sales funnel.
CX Management: Customer experience drives sales adoption, and data dashboards can help track various sales objectives including on time delivery, net promoter scores, average resolution time, and so on.
CLV Management: CLV, or Customer Lifetime Value is the measure of the longtime value of your clients. Data science helps uncover subtle differences that cause the value of one customer to be different from the other, and how a business can help retain high value customers while enhancing the value delivered by the other customers of relatively lower value.
Data science has a pivotal role to play in modern sales. Right from the time you draw your sales strategy, and till after a customer has converted, data-driven insights can offer business a great leg up in the way they operate and succeed. Embracing data science is no longer optional but essential for staying competitive in today's market.
