It is fair to say that CRM updates are often seen as an arduous task by sales teams, however, they are likely to be the people who hold the greatest level of information on customers and deals. But that data doesn’t always make it to your CRM system, or if it does, it can be incomplete or late. This can lead to data gaps, errors or assumptions. It is a particular problem for organisations who use sales effectiveness software, where machine learning AI takes the CRM data that exists and provides forecast insights or generates sales processes to follow, based on perceived opportunities. It goes without saying that without reliable and complete data, the output is unlikely to yield accurate results.
It is difficult to ever think there could be too much data, and perhaps there isn’t, but we’ve often come across companies who are collecting more data than they need or know how to use effectively.
This can pose a particular challenge when using sales forecasting software. Too many data points can negatively influence the output, resulting in inaccurate results. In this instance, the software is using too much data to try and identify sales patterns, but irrelevant data can detract from the truly important information, placing the emphasis on the wrong information and providing misleading results. High volumes of data don’t always bring greater insight.
So, it is evident that having the right level of accurate and appropriate data is key to gaining valuable and precise sales insights. Organisations not achieving this are likely to be missing out on important sales intelligence. To overcome these hurdles and unlock some of the important insights that are often hidden or lost within CRM data, we have some useful strategies.
Three strategies can be applied to your CRM data, that could each unlock a treasure-trove of sales intelligence.
Strategy 1: Make the most of the data you have
As previously mentioned, it can be challenging to get sales professionals and other colleagues to not only input data, but input it accurately – there will always be instances where errors occur, delays are experienced, or data just doesn’t get added. Therefore, making the most of the data you have is a good strategy for gaining more accurate and actionable sales insights.
Have you ever asked yourself what useful data is not being captured in your CRM that could bring greater sales opportunity intelligence? CRMs are a great tool for gathering data, but often discard valuable information that could inform more accurate planning and sales activity. For example, identifying how many times the close date was pushed out, whether a lead ‘converted’ within 1 day, or when in the process a competitor was identified. These could all provide a new perspective on how and when customers are really converting, whether there are any trends, and where there may be data-inputting issues that need addressing to gain a clearer picture.
Using a system such as Cloudapps Sales Insight can help gather and make sense of this kind of intelligence so that it can be used alongside general sales data for maximum effect.
Often, the data we’re collecting has an additional layer of useful, hidden information within it that CRM systems aren’t sophisticated enough to pick up on. Asking yourself whether there is a relevance to when an activity happened, data was input, or a behaviour was recorded, could offer new insight into sales funnel activity. Were these recorded at the beginning or end of a month, or quarter, for example? Was there is a time sequence to these happenings? There could be a correlation that is being missed by not looking at the hidden intelligence within your CRM database.
Strategy 2: Collect more data
When your data is incomplete or you just don’t have enough of it, the obvious solution is to collect more. There are two options at this point;
Buy more data
The most straightforward step is to purchase more data. Suppliers such as Dun & Bradstreet can add a level of data that compliments what you have already, helping to full in the gaps and generate greater insight. Buying leads, in-depth company intelligence, or analytics will support greater customer understanding and potentially short-cut the need for lengthy sales research by your own sales teams. What can be more challenging, is gathering the more behavioural insights that will fuel the most valuable insights about your customers in relation to your business.
Gather more data
Gathering additional data might seem a challenge when sales teams are not currently engaging with CRM data entry, however, this is where sales motivation tools come in. Sales professionals are a competitive bunch, so using league tables, rewards, competitions and gamification techniques within your CRM can encourage the gathering and inputting of a wealth of useful data. Data that will bring you intelligence that is specific to your organisation and your customers, which is invaluable for sales acceleration.
Ultimately, if you are lacking in data, the most practical solution is a combination of both options above, to give a balance of intelligence and allow for well-informed sales activities that actually drive growth.
Strategy 3: Use Deep Learning AI
There are plenty of software solutions out there that offer the opportunity to gain greater insights from the CRM data you have, to improve sales effectiveness. Most of them depend on you having a minimum quantity (2 years) of high-quality data already gathered to be able to deliver useful results because they use machine learning artificial intelligence, underpinned by pre-programmed assumptions, that looks for and draws conclusions.
This is fine if you have that level of data, but as identified above, many organisations don’t have the quantity or quality of data required. However, using a more sophisticated form of artificial intelligence can open up a new level of insight that can transform sales activities – allowing you to claim back up to 60% more selling time and achieve an impressive 95% forecast accuracy.
Start with an open mind
One of the challenges with standard AI solutions is that they need a level of pre-programmed assumptions to be able to analyse your data and draw conclusions, and as mentioned above, this can restrict the level of insight you can gain from your data. The machine learning will only be as good as the person who programmed it and restricted to the parameters it has been given.
With deep-learning AI (or Personalised Science as we prefer to call it), the starting point is quite different. There are no preconceived ideas about which factors might be influencing sales, but instead, a pre-analysis of the data to highlight where gaps may exist or common misleading information may lie (such as sales teams adding their close dates at the end of the quarter if unsure when their opportunities will close), then the machine does the rest. Because it isn’t restricted by pre-programmed assumptions, it uses its own judgment to identify which factors are actually influencing sales, bringing a level of intelligence that can reveal some surprising, yet fact-based, actionable insights.
One of the greatest benefits of this kind of solution is that it doesn’t need huge volumes of data to achieve meaningful results, however, the more data you can supply, the more it can learn – giving you an even greater level of accuracy.
And by providing it with time-sequenced data helps deliver an even greater level of intelligence – allowing the system to not only identify factors or activities that influence a sale but the order in which they happened, or the relevance of dates and times that actions took place. It enables the deep learning AI system to build up a picture of what happened to each opportunity through its lifecycle rather than just looking at a snapshot in time, offering far greater sales insights overall.
Deep learning AI software epitomises the term ‘machine-learning’ because it really does continue to learn over time – with more data, more findings, and more results it keeps digesting new information and re-evaluating to ensure it is considering every relevant piece of information. It uses human-like logic but goes beyond human capabilities by processing millions of pieces of data in minimal time, and identifies correlations, and draws conclusions that we could never achieve. It’s the nirvana of true data comprehension, and the best way to unlock the hidden gems within your CRM system.
Cloudapps is the first and only provider of ‘Deep Learning’ powered AI in the CRM sector.
Trust comes from reliability.
The Cloudapps AI engine is unique in its ability to generate forecasts that are over 95% accurate.
Our Deep Learning AI engine delivers unprecedented levels of accuracy. Powering our customers to achieve results that include:
- 95% forecast accuracy
- 60% more selling time
- 20% increase in win rate
All with a level of accuracy you can trust.
Why not challenge us to show you the power of the Cloudapps Sales Effectiveness Platform by booking your very own live demo.