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Calculating ROI for Sales Intelligence

Sales

1 November 2017

The 2016 Sales Performance Optimization Study by CSO Insights found that only 57.1% of B2B sales reps made quota last year. You might think that number is understated, but the rate was only 58.1% in 2014 and 58.2% in 2015. What’s more, B2B sales teams only achieved 82.7% of plan commitment last year. As sales professionals, we seem to have trouble reaching our goals.

When asked how they would turn things around in 2016, 58.4% set as a top goal, “capturing new accounts” and 40.2% said “improve sales effectiveness.” Both of these goals are supported by sales intelligence services which assist with lead qualification, account planning, messaging, and selling deeper into organizations.

The next three most cited objectives: “optimise lead generation” (39.3%), “increase existing account penetration (30.0%), and “improve customer loyalty/satisfaction” (21.9%) are also addressed via the implementation of a sales intelligence solution.

In my last blog Why budget for Sales Intelligence in 2017, I discussed benefits across sales and marketing. While it is useful to understand the broad set of benefits, it is also important for purchasing departments to convert these benefits into hard numbers. Oftentimes, this justification involves calculating a Return on Investment (ROI). The ROI looks at several years of cash flows to determine the efficiency of an investment. As such, the ROI of different investments can be compared to determine which investments are likely to have the highest returns.

Calculating ROI

Put simply, the ROI is calculated as

ROI = [Benefit/Cost of Investment -1] *100

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More complex calculations build in a discount factor (interest rate) as costs are often front-end loaded with the benefits accruing later on. Fortunately, with cloud-based information solutions, the front-end costs are low and sales reps can begin using the information service almost immediately. While there is some initial investment in training and customisation, the cost structure is fairly flat and benefits are timed with costs, allowing us to skip discounting.

To calculate the ROI, you will need an estimate of the fully loaded cost of the average sales rep. This includes salary, commissions, benefits, wage taxes, annual expenses (e.g. travel, computer, office space), etc. You will also need to convert this to an hourly rate by taking the fully loaded rate and dividing by the number of hours per year the sales reps work. Thus:

- R = Hourly Cost per Rep = Fully Loaded Cost Per Rep / (260 – Holidays – Personal Time Off) * 8

Let’s begin by looking at the costs of sales intelligence services from the sales side:

A = Cost of Service. Service licensing is generally a function of the number of sales reps. As the number increases, the marginal cost per seat declines. This number is generally easy to find on any SOW or pricing offer.
B = Sales Operations Costs = These costs are mostly related to implementation so occur in year one. They include allocating seats, installing CRM connectors, customizing field maps, setting routing rules, creating custom reports, scheduling batch updates, etc. The good news is most of this work is done in the first few days and generally does not need to be repeated. The exception is seat allocation which is required every time an employee joins or departs, but this is a fairly trivial expense. For the most part, these costs tend only towards a few days of work for your CRM Admin or Sales Operations Manager.
C = Training & Personalisation Costs = Generally 5 hours in year 1 and 2 hours in subsequent years. Factoring in some attrition amongst sales reps, I’d estimate the annual cost as 4 hours. Thus, C = 4R * Total Reps.

Now let’s look at the benefits. These can be broken down into soft and hard benefits. Hard benefits are measurable and included in the model. Soft benefits are often intangibles that result in better performance but which are difficult to measure directly. In my last blog, I discussed both efficiency and efficacy benefits. It is the efficiency benefits which generally can be modelled and the efficacy (selling more effectively) which only show up in the final results.

Soft benefits include

Timing calls to accounts based upon sales triggers
Improved account planning based upon access to strategic account intelligence
Extending beachheads to additional departments and contacts at an account
Reduced channel conflict due to misrouted leads
Improved segmentation and analytics within your CRM

On the hard benefits side, improving the efficiency of sales reps is necessary. CSO Insights found that only one-third of sales rep time is spent actively selling. Thus, simply reducing busywork by one hour per day by eliminating account, contact, and lead data entry; streamlining account prep; automating account awareness; reducing time spent on lead qualification, and facilitating prospecting (e.g. finding additional targeted contacts at accounts) results in significant sales leverage. I have conservatively placed the gain as one hour per day.

Over a year, the resulting efficiency gain is 46 * 5 * R or 230R per rep

Thus, a back of the envelope ROI for just the sales team puts the ROI at

ROI = [(230R * Total Reps) / [A + B + (4R *Total Reps)] – 1] * 100

You are welcome to adjust this model, but what is clear is that a modest decrease in busywork can result in a high ROI without making assumptions about efficacy improvements.

Marketing Department

This model focused only on the sales department, but with ABM and increased calls for sales and marketing alignment, sales intelligence services have extended their capabilities into the marketing department. Brevity prevented me from providing a basic model for the marketing department, but you can build one in a similar manner. Just note that your cost structure is tied to the volume of data in your marketing automation platform and the frequency of updates. It also is tied to the number of new leads entering your MAP which are enriched. All of these can be calculated.
On the benefits side, there is a reduction in spend due to marketing costs associated with bad leads in your MAP (e.g. the company went out of business, the contact left the firm), lowered webform abandonment rates due to reducing the number of fields, and higher conversion rates due to improved prospecting, targeting, and messaging.

Net

What is clear is that with sales reps so pressed for time to engage in direct sales activities, a small improvement in sales efficiency can justify investments in sales intelligence tools even without modelling for soft benefits.
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