Seven best practices for better sales analytics
In days gone by, sales management was definitely more of an art than a science and one centred very much on gut feel.
But a new generation of sales directors is now starting to come through. They are keen to get their hands on company data in order to help them make smarter decisions and boost the performance of their sales teams based on facts rather than feelings.
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Because the adoption of sales analytics tools - beyond the seemingly ubiquitous spreadsheets - is still very much in its early days, however, here is some best practice guidance to help steer you through some of the most common pitfalls.
1. Take your sales analytics initiative one step at a time
Sales analytics tools can help you track and understand the behaviour of your sales force and also give you insights into why customers buy what they buy.
But there are four key types of tools on the market to help you did this and Gartner splits them into four categories, each of which is on a continuum:
- Descriptive – What happened? For example, which key opportunities did we win or lose?
- Diagnostic – Why did it happen? For instance, we won or lost due to competitive pricing.
- Predictive – What will happen? For example, win rates are likely to improve next quarter.
- Prescriptive – How can we make it happen? For instance, we will improve win rates due to more effective value selling.
This means that it is usual to start your project at the descriptive and diagnostic levels and, once experience has been gained, to move onto the next stage.
2. Ensure your data is complete and accurate
Any sales analytics tool will only be as effective as the data entered into it from sales force automation and/or enterprise resource planning systems. But many companies tend to employ such tools in a rather undisciplined way, failing to provide sales staff with simple, structured templates laying out the data they want to collect.
It is also worth focusing time and effort on ensuring that the quality of your data is high by undertaking a data cleansing project on existing information and making certain that it as complete and accurate as possible. Failure to do so could mean that answers to your questions are not reliable.
As part of this process, also take the time to agree on standard, objective terminology. The idea is to ensure that everyone understands what basic words and expressions such as “qualified leads” mean in order to avoid misunderstandings later.
3. Understand what outputs will help you attain your objectives
All too many organisations go out and buy an expensive sales analytics tool and then sit back and wait for the magic to happen. But in order to make the most of it, it is very important to have a clear view of what you are trying to achieve – so what are you using the tool for and to what end.
To help things along here, consider which part of your sales process needs attention, either because it is poorly-understood or broken. Then, come up with very specific questions about that area in order to gain a clearer idea of what is going on. Such questions could include how long it takes on average to close a deal and which customers buy what products where.
4. Ensure you have sales operations expertise in place
If a given sales manager does not have sales operations expertise, it may be helpful to bring such skills in. Sales operations personnel are logistical, process-oriented people who provide discipline and structure when working with sales analytics tools. Many are data scientists, or are in the process of becoming one, and undertake activities such as preparing reports, evaluating sales enablement tools and setting up systems.
5. Handle change management carefully
In order to get hold of the right data for analysis purposes, it is crucial to ensure that sales staff keep their records updated with pre-defined information such as potential opportunities at each stage of the sales cycle.
To encourage them to do so, make it clear that the organisation now relies on such information, which means that it is considered a core responsibility to accurately record their activities in order to ensure that the team, and the company itself, performs better as a whole.
Tied to this, however, is the need to prove these statements by being seen to act on the data provided. For example, if an individual is found to have problems in converting leads into sales, use the data as the basis for discussion and offer to send them on a training programme to aid their professional development.
Also important in this context though is the sales leader’s management style and approach, which means that training may be required here too. If change management is not handled sensitively and the team feels that data is being used against them in a Big Brother-ish fashion, they will find ways to subvert the process or simply start sandbagging.
This means that helping personnel to understand how data can be used to their benefit is crucial. One approach is to openly share dashboard information in your weekly catch-up meetings rather than waiting for quarterly team meetings in order to keep people in the loop.
But bear in mind that using data to inform discussion can also change the nature of the conversation too. So if someone is not hitting their sales targets, the focus no longer necessarily has to be on asking why. Instead it can move on to offering possible solutions such as altering the size of territories or focusing on repeat business.
6. Keep things simple and iterative
Large corporate sales analytics projects that are led by IT departments often fail because they can become over-scoped and over-specified and just too complex. The problem is that, rolling out functionality based on everyone’s pet features all at once can result in the initiative simply collapsing under its own weight.
But keeping things simple and limiting the initial scope of initiatives generally works well. The old adage ‘deploy early and deploy often’ applies here, with each stage of the process comprising activities that are linked to key deliverables, which are made available at the end of each phase.
This means that new features are rolled out iteratively in chunks on a must-have basis, which in turn enables you to understand what has been used and what works. Managing this process is likely to require an experienced project manager or subject matter expert in order to keep nice-to-haves to a minimum, however.
Over time, data from other departments such as marketing and customer service can also be added in order to encompass the entire business, but the key lesson here is that it pays to start small.
7. Remember it is the data that will give you sales insights, not the tool per se
Your sales analytics tool will show you patterns in data and answer your specific questions, but it will not be able to interpret this information for you – that is down to you. But it is in interpreting and acting on the data provided that the real power lies as it will help to inform your sales strategy.
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