3 Ways You Can Optimize Your Sales Team with Analytics
Analytics organizations that make the effort to support their sales teams can elevate their status within an organization.
- By Troy Hiltbrand
- May 31, 2022
The role of sales is not an easy one. It is fraught with challenges, such as being ghosted or outright rejected by prospective clients. However, even though it is challenging, sales holds an important position in the organization. Organizations rely on their sales teams to generate revenue, without which they can’t remain viable as a business.
Because many technologists are introverts by nature, they often do what they can to stay out of the sales space, the realm of the extrovert. They work to stay in the background and keep out of the spotlight of sales-customer engagements. This leads many IT departments and their associated analytics teams to be caught in the trap of being labeled as cost centers that don’t generate revenue.
This doesn’t have to be the case. Analytics professionals can help their sales teams convert prospects into revenue. This can transform the way the organization looks at IT -- as a revenue center instead of merely a cost center.
With advanced analytics, here are three things you can do today to help your sales team generate more revenue: lead scoring, price optimization, and customer journey analysis.
Lead Scoring
Even before a sales relationship with a prospective customer begins, prospects must be identified and courted. Great salespeople often lean on their intuition and gut feel when it comes to finding and evaluating prospects. Intuition alone can be powerful, but it shouldn’t be the only mechanism that sales teams use. By pairing this intuition with data analytics to pick up on more nuanced and non-intuitive indicators, sales teams can take their practice to a whole new level.
This is where lead scoring comes into play. A sales lead is defined as a potential client who would be interested in your product or service or an existing client who would be interested in an expanded set of products or services. Not all leads are equal and applying the same level of effort to all leads is an ineffective process. Sales teams want to know which of their leads has the highest probability of converting and ultimately contribute revenue.
Sales teams often see, evaluate, and rely on conversion indicators that happen at the surface level, such as in their direct interactions (in-person or virtually) with the prospect, as well as phone conversations, email exchanges, demos, conferences, and meetings. The sales team evaluates the content of those messages and conversations, the body language of their prospects, and any historical context around the relationship to make a judgment call on the value of the prospect.
As an analytics professional, you can augment those person-to-person assessments with additional qualitative and quantitative features. These additional features can include actions and behaviors that the prospect takes outside of those direct engagements -- actions such as browsing your website, viewing and engaging your ads, searching for independent reviews of your products and services, and comparing your offering to that of your competitors. Being able to capture and correlate these activities and behaviors to specific prospective customers gives you more facets to the lead scoring algorithm.
With this combination of person-to-person attributes and systematic monitoring, you can develop a full profile of each prospective customer. Having this map of attributes about prospects who historically did and did not convert allows you to start to establish a predictive analytics model. With this model, you can generate a score for each lead that essentially represents the probability that they will convert. This enhanced score can focus your sales team on those prospects who are most likely to become actual customers.
With a predictive model, you can also identify which features have the greatest positive impact on conversions and arm your sales team with the knowledge of where to focus their time and attention to close more deals.
This predictive model can also identify markers of activity that have a potentially negative impact on conversions. These negative indicators during the sales process could represent a warning sign that triage is needed to save the relationship. Minimizing these negatives can potentially have as much impact on closing the deal as accentuating the positive indicators.
Price Optimization
Prospective customers have different budgetary constraints and price sensitivities. Successful sales often require that sales teams negotiate the price of the final good or service to that point of equilibrium where the prospect’s demand matches an acceptable price to warrant the company’s supply.
At times, the sales price of the good or service is fixed, but sales teams have other levers they can use to make an attractive offer, such as discounting, sales incentives, and financing.
From an analytics perspective, your job is to use historical data to identify how close a negotiation is to that equilibrium point and provide insight into the approval process for additional discounts or incentives. This data augmentation should include as full an understanding as possible of who the prospective client is, who the competition is, which competitors the prospective client has engaged with, and what the current environment is around the negotiations. When combined, these analytics can more accurately predict what price point will optimize the relationship between your company and the prospective customer.
A sales manager armed with these types of key data points and ensuing analysis will be able to augment their intuition. This will lead to better optimization in revenue generation.
Customer Journey Analysis
Not all sales involve new clients. There is significant value in continuing to nurture existing relationships with clients and offer new revenue opportunities at the right time. Analytics professionals can monitor existing relationships and evaluate key metrics such as propensity to churn to alert the sales team of optimal times to engage. The better that the analytics team knows and understands internal data about the client (such as service or product utilization) and external data (such as client management structure changes or budgetary and revenue cycles)\, the better they can enable the sales team to engage at the most opportune points along the customer journey.
As new products or services are launched by a company or expanded offerings become available, analytics teams also can generate measurements for each existing customer related to the propensity to buy. With this data, a sales team knows who to engage to demonstrate the new or increased value of the new offerings.
Predictive and proactive maintenance of sales contracts allows the sales team to reach out to key clients at just the right time and further grow the relationship. Those organizations who rely solely on contract expiration dates as an indicator of when to service the client do not have the same potency and impact as those who can more effectively time their engagements to periods that better match a client’s demand cycles.
On the inverse side of that, analytics teams that can monitor the cost of serving a client and raise warning flags of clients whose business is costing the company more than it is worth can optimize their portfolio of clients. This could happen through renegotiation of existing contracts or optimization of service offerings to reduce the existing service costs by an informed sales team.
A Final Word
Although analytics professionals do not always want to be directly involved in dealing with clients, either on the front end or throughout their life cycle, they are well-positioned to provide key metrics and measures at opportune times to increase the effectiveness of the overall sales process. Although IT is often viewed as a cost center, the direct contribution to augmenting the sales team through analytics is one way that IT can show itself indirectly as a revenue center. This can greatly enhance the role of analytics and IT and elevate and enhance its reputation.