Personalize your learning interventions using learning analytics
Sales learning analytics: how measurement can drive performance
Training is meaningless unless it measurably improves employee performance and productivity. In today’s hyper-competitive world, training organizations are no longer being asked to produce content, but rather mandated to solve critical employee performance issues.
Thought-leading CLOs are assuming performance advisory roles and are investing in productivity research to inform themselves and the sales management on
- What are the key performance issues,
- What are the most relevant and effective interventions required and
- What is the impact of the interventions initiated?
This approach is not new but we are seeing a paradigm shift in how the same decisions are being made.
Sales driver analysis: where are the gaps?
Traditionally, learning strategists rely on extensive interviews to understand the performance gaps of the sales teams while the sales leaders articulated their most recent experiences with the sales team. The interview method facilitates collaborative soul searching and yields a comprehensive list based on the coverage. However, it has the following issues
- it relies on what the sales team knows and does not ensure latent issues to come to fore
- the only way to prioritize issues is by taking a collective poll or by going by the sales heads priorities
- We can identify collective issues and not individual sales person’s issues. This leads to one size fits all solutions.
In short, the interview approach is consensus and opinion driven and not very scientific. The learning team sees itself taking orders and developing programs with great reviews but unwilling to be held responsible for hard sales numbers while claiming to fill competency gaps.
New-age CLOs are employing advanced statistical analysis to understand the true drivers of sales performance and uncover statistically evident performance gaps to inform the sales leaders of development areas, as opposed to taking orders.
As many as 110 variables for CRM, HR, learning, and customer factors are modeled using statistical techniques such as neural networks, random forest, CART, and logistic regression to study their quantitative impact on quota attainment.
Sales DNA Diagnostics (SDD) provides sales and learning leaders a prioritized list of performance areas to focus on, with confidence, and expect positive performance improvement when the gaps are addressed.
Sales performance intervention: when and what can you do to help?
Once sales improvement areas are identified, sales leaders sometimes even recommend a certain program that they have gone through in the past, or have heard of, like CXO selling, solution selling, challenger selling, power base selling etc. Learning leaders are pressured to try “something new.”
After developing the programs or sourcing from established vendors, the training team is focused on filling seats. Training compliance seems to be the single most important metric. The second most important metric being participant rating. This leads to all the innovation around training delivery – games, simulations, role plays, best in class speakers etc.
This approach primarily assumes
- one size fits all
- one time intervention is enough and hence programs are designed for maximum recall
- it will work—something is better than nothing.
Armed with data and insights down to each sales rep level, new age sales learning leaders are now able to personalize interventions by rep as opposed to a one-size-fits-all approach.
Sales prediction models can accurately predict which sales reps will not meet their quota and why. The reasons could be competence related, deal related, customer related or even point to macroeconomic factors.
Sales Prediction and Recommendation Kit (SPARK) can help sales managers and mentors coach each rep on competencies they should develop and how. Sales reps can also get action guidance on each deal and recommendation on which accounts to pursue to make their numbers.
Business impact analysis: how do we know it worked?
Most learning organizations use the terms learning evaluation and learning analytics interchangeably, as this is the only area in which some data analysis is done. Learning evaluation models are widely available (Phillips, Kirkpatrick, Brinkerhoff, Bersin). Learning dashboards track the no. of trainings published, hours of trainings offered, compliance percentage, participant rating, facilitator rating, content rating, content reviews etc.
While these are critical measures, learning dashboards are yet to capture and show the business impact of each of the courses. The reasons being
- lack of understanding regarding which business metric to measure for each course
- lack of a measurement strategy that can isolate the impact of learning from other influencing factor
- fear of not showing positive results
- learning organizations not assuming responsibility for business impact
Having designed and implemented programs that are most relevant and having offered individualized coaching, sales universities are more confident of creating measurable business impact and being held responsible for it.
Data-oriented sales universities measure the business impact of each of their courses on sales pipeline metrics like no. of opportunities created, average deal size, sales cycle, conversion ratio, percentage of new business revenue, percentage of strategic products sold etc.
A variety of measurement strategies like before-and-after analysis, with and without analysis, intervention analysis based on time series approach etc. can be used to measure the impact. However, the most critical aspect is to be able to isolate the impact of learning from other variables that may have positively impacted performance like seasonal trends, change in compensation structure and leadership etc.