(This article originally appeared on LinkedIn Pulse and can be found here.)
Sales data is often messy and even at times incomplete. In that state, it can be unusable for predicting outcomes, which can be frustrating as sales sit at a crucial point in the customer journey and offer important data. Some of the mess is caused by a failure in automated data capturing processes but a lot of the problem is rooted in people themselves. Sales are still largely human-to-human and require great effort to quantify because humans are complicated creatures! But with the right data, sales teams can achieve glory and experience amazing efficiency gains by overcoming typical roadblocks to build models that identify leads which convert at higher rates and higher lifetime value.
Roadblocks When Data Doesn’t Exist
- Don’t Believe in Data – a lot of sales teams still rely on gut intuition alone. There is a lot of power intuition but it is not a magical solution. Rather it is reflective of the person’s ability to absorb and process information. Data is just a concise form of information and when added to intuition makes a powerful move.
- Everything Happens offline – Sales can be strictly relationship based on deals happening at trade shows and in boardrooms. Data can still be used in these situations, it just needs to be quantified and manually entered into a Customer Relationship Manager or CRM.
- Presumed collective laziness – All the sales teams we spoke to reported a struggle to get information into a CRM. Some chalked it up to human laziness but contradicted themselves by rating their team members as highly persistent and hardworking. When asked to expand, individual salespeople report a backlog of work as the main obstacle but assumed overall failure to enter data was due to laziness.
- Don’t know what data is worth collecting – Sales teams reported that there was no clear strategy or method used for identifying metrics that matter. They also reported failures in attempts to adopt “one size fits all” metrics because their sales processes were unique and the data never got used or collected efficiently.
Roadblocks When Data Exists
- Dealing with Unstructured Data – because sales are still a human-to-human process a lot of the data reported was unstructured. Examples of this are emails, voicemails, and their comment/notes in CRM. The teams interviewed unanimously said they did nothing with this data. Although individual salespeople were highly interested in the insights that could be gained from it.
- Fields, not forms (when possible) for manual data entry – By standardizing manual data entry you achieve the quickest win for your team. Written text entry results in hundreds of unique values that are hard to analyze. This roadblock can be avoided altogether by using a drop-down option or state validating form fields that create a standardized entry of 50 unique state abbreviations that are analysis ready.
- Not automatically capturing things – Product and marketing teams typically have their own data scientists and devs for this exact reason; to automatically quantify manual processes so the resulting data can be used to hunt down and kill inefficiencies. Sales teams should be no different and should consider using such professionals as well.
- Time series nature of the data – the few teams that succeeded in getting data into a CRM and mining it ran into another roadblock – modeling human behavior in the sales process over stages of time. While it complicates the analytical approach it doesn’t make it impossible, so long as the data is organized correctly.
Teams should also be wary of the promise of a “one size fits all” solution. On the low of the data maturity stage, it’s important to avoid dashboard dependency and dig deep into that data to make predictive models. On the high end of data maturity is the yet unfulfilled promise of Artificial Intelligence (AI). Teams also reported roadblocks when working closely with other departments to share critical information and creating feedback loops. Conquering this roadblock is another quick win as the exchange of information between unique departments furthers growth in all respective departments who can utilize it.
Becoming a Data-Driven Sales Team
You can’t possibly model a problem and predict an outcome if you don’t understand the context (origin, scope, totality, and measurement) of the data you’re using. Using common frameworks facilitates interfacing with other departments. They force you to combine data to overcome departmental divides and create huge efficiency gains through better predictive modeling. Honestly assess your position in the data maturity states then test/iterate from there. If you’re persistent enough in your human effort you’ll avoid the typical roadblocks sales teams experience and the quality and quantity of your data will become increasingly impressive.