With all the advances in sales technology and analytics, you would expect sales teams to have all the data they need about each sales opportunity to accurately forecast sales. However, that isn’t the case. Research by Bohanec, Kljajic Borstnar and Robnik-Sikonja in the study “Integration of machine learning insights into organizational learning, A case of B2B sales forecasting” found that:
“typical CRM implementations are missing sales opportunity’s attributes reflecting relationship dynamics, individual and organizational attributes and thus a context of particular case is not described in a suitable way for machine learning techniques to perform adequately as the existing CRM attributes have low information and prediction value”. (Bohanec, Kljajic Borstnar, Robnik-Sikonja, 2015)1
Types of Data to Be Collected
Before reviewing best practices for collecting data, we need to review "what" the data looks like.
We define what the above quote calls “(1) relationship dynamics, (2) individual and (3) organizational attributes” as: (1) Sales activity dynamics; (2) opportunity attributes; buying team members and roles; and (3) company attributes. The following provides a description of each area:
Sales activity dynamics
Sales activity dynamics include all interactions with the prospect. Information should include interactions between members from both the buying and selling team including the detailed content of the interaction and the ability of the sales rep or system to be able to ranking/ indicate the quality of the interaction. Sales activity dynamics include things like:
Opportunity attributes vary based on the selling company, the industries they sell, the customer categories they sell, the sales cycle and the products they sell. They include things like:
Opportunity attributes are any attributes, either standard or custom that are required to accurately describe the opportunity
|Buying team members and roles||
Although this is really part of the opportunity attributes it is broken out separately because of its importance. Our customers have found that having the right buying team members at the beginning of the sales cycle is critical to an efficient sales process. Results show, not working with the complete buying team up front, and having members added to the team towards the end of the sales cycle generally restarts the sales cycle.
Company attributes help describe the company and include things like:
Company attributes are any attributes, either standard or custom from the account record in the CRM application
In summary, the type of data to be collected is “any attributes at any level that impact the sales process”. It could be transactional in the form of interactions during the sales process(sales activity dynamics), or it is attribute based to describe the opportunity or company (opportunity attributes, buying team members and roles, company attributes.
The Beatings will Continue Until Morale Improves
It is easy to define and theorize about the data needs. Collecting it is the real problem.
Having sufficient, timely and accurate information about all the sales opportunities and storing it in the Sales Force Automation platform [SFA] comes at the expense of the sales rep’s “selling” time.
The easy answer is to hold sales reps accountable for accurate and timely updates, but that fails on implementation. The choice generally gets down to, do you want accuracy in your SFA platform or do you want sales.
SiriusDecisions has done significant research via a sales activity assessment and time productivity study on sales reps, sales development reps and sales managers. Its objective was to understand the real issues impacting the sales group’s daily activities and the root causes when time is used inefficiently and limiting sale effectiveness.
The research found artificial intelligence and automation embedded in sales planning/sales force automation solutions can: simplify activity management; eliminate the manual tracking and reporting of sales activities such as emails, phone calls, face to face meetings and web conferences in the CRM solutions. In summary it can help optimize time spent direct selling by reducing administrative time entering and managing the data.
General Guidelines For Data Access Best Practices
Before we dig into tactical best practices for data collection I want to set the general guidelines of the best practices:
- First automate the data collection process wherever possible
- Second, if automation is not possible, make it as easy as possible for the collection process as part of what the sales reps do now, not in addition to what they do. Work where they work, don’t require them to log into a computer to perform updates, let them dictate into their phone.
- Third, make sure there is something in it for the sales rep when they keep the information accurate, give it back to them in a form that helps them do their job. Typically updating the CRM is for the sales manager.
- Fourth, the data collected needs to be delivered in a highly intuitive, easy to use format that is consistent with how the sales reps use the information.
Where Do We Go From Here
The next couple of blogs will drill into specific tactical best practices for each of the four data categories identified above.
If you are new to this blog series, check out our first blog in this series: Best Practices for B2B Sales Pipeline and Forecast Management.
1Bohanec, Marko. Kljajic Borstnar, Mirjana. Marko Robnik - Sikoja. “Integration of machine learning insights into organizational learning, A case of B2B sales forecasting”. 28th Bled eConference, June, 2015. https://domino.fov.uni-mb.si/proceedings.nsf/Proceedings/B12ECF2381AB59EEC1257E5B004B39B7/$File/2_Bohanec.pdf