Sales professionals rely on a range of things to be successful. This varies from a good product, and rep’s own personal skills, to efficient processes and technology. To help sales reps be more effective, organizations have made huge amounts of investment in customer relationship management (CRM) systems and other technologies.
According to Gartner, the worldwide CRM market totaled $23.2 billion in 2014. As part of these investments, sales teams are tasked with recording and ensuring the accuracy of customer data; much of which is harnessed to improve sales operations and business efficiency. However, this information is mostly used by sales leadership to facilitate decisions, and is not widely leveraged by the frontline sales staff.
There is tremendous opportunity in data that can help sales professionals become in control of their success. However, this is where the problem lies: if you offer sales people a new process, or best practice, or behavior, they will carry it out as long as it makes the best use of their time. In other words, unless they see direct benefits from it, they will not take full advantage. This is true about data as well. You have to make it about the rep to make it happen. Sales professionals have to see the value of data and analytics, in order to invest their time, attention and energy.
Let’s see how you can do that.
Using dirty data to incentivize sales reps and stop bad behaviors!
One of the common pushbacks with analytics has been data quality. This sentiment should be familiar for many: “we cannot properly analyze and make decisions until we have corrected the quality issues in data.” However, there should be no surprise that analytics is often based on incomplete or ‘dirty’ data. The first step in dealing with this is to accept that information will never be perfect. In fact, dirty data is actually preferable to start with, as long as you set the right expectations.
The act of starting to analyze data, even with all its imperfections, provides insight into broken processes, inefficiencies, and problem areas. This is often more powerful than the effort to clean huge amounts of information prior to analytics. For example, by analyzing sales data, you can realize that your reps have not been recording customer information completely, and fields like ‘industry,’ ‘company size,’ or ‘proposed products’ are missing from your data. You can also discover that some reps have the habit of over-promising, while others usually under-promise their targets.
Part of this is also personal. When sales professionals see and understand the impact of good quality data, and its positive effect on their work, they are incentivized to enter more accurate information in the first place.
Setting up lengthy data quality initiatives, or postponing the implementation of sales analytics until the data is clean will just perpetuate the problem and stop the whole sales team from getting to value quickly. It is therefore important to realize that your data will never be fully cleansed, and while you should embark on a data quality initiative, you should concentrate on getting started with analytics despite all the necessary caveats in place.
Wall of fame / shame: improving performance with peer-to-peer comparison
Peer-to-peer analytics creates a great platform to help individual reps take more ownership of their performance. To make this successful, managers need to ensure that they are segmenting reps by regions and cohorts. You would not expect a new rep to perform at the same rate as a fully ramped rep; or a rep in more lucrative markets to perform the same as a rep in less developed markets. To minimize false results, you should also consider the analysis timeframe. Sales targets and market dynamics change often and therefore prior performance can only be a factor of future performance.
Because of their personal nature, peer-to-peer scorecards are in particular the type of analytics that increase adoption and use of data within the organization. With this type of analysis, you will soon see your team logging into the analytic application, and even starting to create their own reports to show the impact of their initiatives on revenue.
This is great! However, management should consider putting enough guardrails around the data to make this a guided experience. Last thing you want as a sales leader is to have a rep arguing that their numbers are more accurate than yours. Without having a “single version of the truth”, it is difficult to gain consistency, so you end up reconciling data, instead of selling!
Scorecards can also be very powerful for you as a sales leader. For example, seeing that sales people across a team work in different ways can help you realize the behaviors and steps that successful sales professionals take, and encourage this behavior in others through building appropriate compensation packages, training, and support programs, or simply applying best practices from one region or group to the other.
Sliding data into daily sales activities: dress, plan your day, and get your data face on!
Each business runs its sales and operations teams differently. A company that provides one-off products will not have the same needs as one that offers services or a firm that is built on a recurring revenue model. However, all organizations rely on their sales in order to achieve revenue and meet profitability goals.
In addition to using data insights for your sales team’s performance improvement, it’s possible to give sales professionals prescriptive guidance on how to improve their chances of making a sale happen. This can include more strategic suggestions around particular vertical markets where the demand for a product is higher than others, or tactical ideas like product bundling and upselling of certain products that have sold well together in the past.
Similarly, as sales people make hundreds of calls a day, using data and analytics can be very beneficial for deal scoring, revealing which opportunities are worth chasing and which ones should be allowed to mature as part of marketing nurture campaigns. For sales professionals, the feeling that certain calls are more likely to be successful than others can increase confidence and result in better customer interactions and closing business.
Data can be useful for face-to-face interactions as well. For example, sales professionals can use analytics to handle on-the-spot pricing questions. By showing average prices of recent sales into companies that are in the same region, vertical, or company size as the current prospect, sales professionals can gain tremendous credibility. This is often done using a mobile device right in the meeting. Having these types of data and analytic benchmarks helps sales professionals get more guidance on how willing their buyers are to pay for a certain price and how much flexibility and elasticity they have in accommodating discounts.
Use data to empower salespeople to own their book of business
On the strategic side, data and analytics helps reps determine the strength of their pipelines, quota coverage and lead flow. Even though tracking and ensuring accurate forecasts has been a sales leadership role, giving individual reps the ability to see their pipeline and its influencers, helps them become self-sufficient revenue-owners, and in charge of their success.
This level of insight helps reps identify the profile of their best deals, and recognize key influencers for winning. For example, identifying an ideal buyer, industry, product mix, discounts, sales cycle timelines, and deal structure can shed light in prioritizing the pipeline and helping reps decide whether or not the right mix of opportunities are there to close business. More savvy reps can use analytics to perform “what–if” scenarios and see how changing certain drivers can influence their deals. For example, would changing the discount levels, adding in extra product seats, or extending the terms of an agreement influence win rates, and by how much? Similarly, by having visibility into their lead flow, sales reps can work with their field marketing counterparts to create new programs and campaigns in anticipation of additional pipeline.
The role of data within sales organizations is changing. It is shifting from management reporting to designing a strategic weapon for sales professionals and the wider sales teams. However, this shift requires overcoming initial resistance around data quality roadblocks and gaining a buy-in from across the organization. By treating analytics as an incentive and a motivator that can put individual reps in charge of their own success, sales leadership can build a highly-effective team that makes good decisions, knows how to prioritize and feels the ownership and the responsibility to treat data as a strategic tool towards business value and revenue.
The preceding is a blog post originally published in Sales Initiative on August 24, 2015.