Top sales teams are using data and AI to get insights that will boost productivity and ROI by helping them sort leads more accurately and efficiently.

Sales teams now have the opportunity to find golden insights that can dramatically improve their efficiency, accuracy and return on investment.

Salesforce’s newly released Third Annual State of Sales report shows that the data-led approach is already paying dividends, with high-performing teams 1.6 times more likely that underperformers to prioritise their leads based on data analysis, and half as likely to cite intuition as an influence.

With the help of AI, data can be mined more quickly and easily, giving you more time to build customer relationships that matter, and make the sale.

Increase your ROI with data-driven lead prioritisation


Most sales organisations could be imagined as a pyramid – at the base there are the most customers and the pipeline is fed primarily by inbound leads. But then on the upper part of the pyramid – where there are fewer customers, fewer companies in the market and fewer salespeople – they have to seek out leads and generate pipeline.

One of the key opportunities for both segments is using data analysis to prioritise leads, whether inbound or not. Data can help businesses build a more complete picture and gain a deeper understanding of their customers – their buying patterns and how they’re consuming product, as well as countless other metrics. These can help salespeople direct their efforts to the accounts and actions that are going to pay off the most.

Sales teams can dramatically improve their conversions by identifying and targeting leads that are more likely to make a purchase. The key goal is to prioritise leads that are most likely to drive the highest return on investment – the leads that are most likely to bring in the most revenue, with the shortest amount of time spent bringing them in.


Data brings a new lens to forecasting


Applying a big-picture view of the market to forecasting moves it beyond ‘gut feeling’, to provide a new level of accuracy.

The first step of a solid forecasting process is to work from the bottom-up. Have your sales reps tell you about their level of confidence in their pipeline, assessing each deal against three key criteria:

  • Value: how much the deal is worth

  • Timing: when it will close

  • Certainty: perceived likelihood of the deal closing

The level of certainty should not be based on intuition. Rather, it will be driven by where they are in the sales process. So, if the sales rep has done discovery, understands the customer’s current challenges, presented a solution, had them agree that you are the vendor of choice, and gone through a commercial conversation to negotiate price and contract terms with the customer, then your sales rep will likely be confident that the sale will go through.

They can then share that information with managers at different levels. Through the forecasting process, the deals and predictions get filtered. This is the top-down part of the approach.

The final step is data analysis - and with AI, such as the Einstein AI layer built into the Salesforce platform, that analysis is far more efficient.

It takes forecasting one level further with predictions and recommendations based on lots of different factors, for example taking in historical data and applying different algorithms that look at growth, the economy, how many salespeople you have versus customers, and how much marketing you’ve done in a particular territory or industry. Einstein analyses the data in a deeper way than people do and presents quality data in terms of what is going to happen with a deal – and it may be different to what was determined by the top-down or the bottom-up approach.

Mine your data for golden insights


Not so long ago, being in sales meant closing deals and meeting quotas. While this is still true, the scope of sales is rapidly expanding.

The top sales team KPI in Australia and New Zealand is now customer satisfaction, according to our research for the State of Sales report. And simply selling does not increase customer satisfaction - relationships, advice and personalised experiences do.

Salespeople are expected to build and maintain customer relationships by becoming experts in data analytics and using their insights to provide tailored advice based on their customers’ industry and organisations.

Thankfully, AI can help streamline this processes by turning data into insights. Already, 21% of sales leaders say they’re using AI, and this is expected to grow to 54% by 2020.

However, to get accurate insights, you first need high-quality data. Here are four ways you can get started:

  1. Create a culture of timely and accurate data capture in your organisation.

  2. Ensure your staff know how to turn data into actionable information by providing extra training (check out the sales modules in our training platform Trailhead) or hiring staff who already have experience analysing and interpreting data.

  3. Get the executive team on board, making sure that everyone has access to and is using the most relevant, up-to-date information.  

  4. Create an engagement layer, including customisation and mobile access.

Data doesn’t just need to be high quality, you will also need a lot of it. As the Internet of Things continues to grow, companies will be able to access valuable data from their customers’ fridges, warehouses, machines, cranes, and beyond. By combining data and new technologies, companies will be able to understand and provide for their customers like never before, with greater efficiency and ROI.

Discover the trends shaping sales today in our Third Annual State of Sales report.

Phil Cleary leads sales enablement for Salesforce in APAC, building and delivering on-boarding, product training, business acumen and selling skills programs for sales professionals across Australia, Singapore and India.  

Phil will be speaking at Sales Innovation and Tech Fest in Melbourne 30-31 July.