For the past few years, we’ve heard that Artificial Intelligence is the game-changer that will enable people to work more efficiently. We’ve understood that AI will create more opportunities for businesses to be digital leaders in their respective fields. Creating opportunities is especially true for sales organizations. But, AI is not the holy grail of sales, at least not yet.
Sales reps live, eat and breathe customer relationships.
Striving for excellence in customer relationships is why AI is so appealing to the sales industry. AI can help reps unlock their customers’ needs, see gaps in coverage territories by alerting sales to the regions and customers who need more attention.
From a performance standpoint, AI can enable sales operations to uncover which reps are underperforming and what compensation tactics are needed to boost that performance. But AI’s potential is not all that it may seem. In fact, sales data is falling short, specifically with buyer data and intelligence – a key metric that has a big influence over sales strategies.
If AI is not living up to the hype, then it’s a wonder why we still hear this narrative year after year.
The reality is, AI is only as powerful as the data organizations are putting into their systems in the first place. The data may, unfortunately, not be very good. If the promise of AI can be achieved, it’s just going to take a little longer than most people think.
In order for AI to indeed be a transformative force in business, sales organizations should focus efforts on the basics to establish processes that more accurately collect, store, and analyze only the data. With correct methods, AI can have the potential to make a real impact on their business operations. For many organizations, that data already lives in their sales performance compensation systems — they just haven’t realized it yet.
Look at the steps sales should take first to get their data under control.
Out with the Old
The first step is for organizations to replace legacy systems with automated processes that consolidate and harmonize data. One way to achieve this is by using no-code extract, transform, and load (ETL) tools.
These tools enable sales to quickly validate and pre-process data by giving reps the power to make changes or additions to the data management process themselves without needing to involve expert developers or the IT team.
No-code tools also arm sales with integrated data that can be quickly analyzed to uncover actionable insights, which are imperative for leaders to make more informed and impactful decisions that help sales reps focus on the right opportunities.
Replacing legacy infrastructure.
Replacing the legacy infrastructure may be easier said than done since updating these systems takes time and manpower resources. The long-term rewards are worth that initial headache.
Automated systems free up resources so that sales can focus on executing their strategy to meet company goals instead of wasting time updating manual datasheets.
Single Source of Truth.
Sales create a lot of data, no question. The data comes in a variety of formats that are stored in siloed, disparate repositories. Sales needs to connect those sources together into one “single source of truth.” The real story is not all that hard to find since it already lives in every organization within their sales performance management (SPM) systems.
To get to the single source of truth, sales should implement an automated data management framework that ensures the data gathering process is being consistently updated and cleansed. The data management system makes it easier for sales reps to report their data, leading to a more complete and accurate data set.
It only makes sense that data that is linked to money would be the most accurate because it is the most scrutinized and the most regulated. With insights gleaned from their SPM platforms, sales can make timely, actionable insights to meet their goals.
Clean Your Data.
Once data has been gathered into a centralized location, the sales team needs easy access to glean actionable insights from it. Unclean, disorganized and decentralized data is useless to a sales organization. Lousy data, and can negatively impact compensations and sales operations processes, not to mention other vital business decisions. Cleaning and centralizing data can be a highly technical and very intensive task.
Store clean data.
The key to storing relevant data is not through the storage methodology, but instead through the way the data is processed. Technology that can automate centralization and other relevant data sets can improve the speed of data processing to ensure that sales always has access to the most relevant, accurate, and comprehensive set of data.
Clean, untangled data uncovers insights into past and present performance.
With clean data will allow sales to model predictive scenarios for all aspects of their strategy. It also gives salespeople in the field the information they need to seize high-potential opportunities and close deals more successfully.
Set Clear Goals.
After sales have automated, organized, and cleaned all its data, they must focus on setting company goals with clockwork precision for AI to truly work for their organization.
Sales should clearly define these goals and make them easy to understand by narrowing them down into a single question: what type of event or behavior from the past should be replicated or avoided in the future?
For example, sales can crunch large amounts of historical data about the deals the company has both won and lost. The large historical data helps sales to understand which current deals are most likely to close.
Using the extensive information, sales reps can uncover insights into their highest earning opportunities. Focusing on the deals, they can win and enabling them to deliver the highest ROI.
Access Accurate Macrodata and Microdata.
Before organizations can reap any rewards promised by AI, they must first learn to access accurate, up-to-the-minute macro and micro-level data. Following the steps above is the key to unlocking critical business indicators that lead to informed, strategic decisions that maximize sales opportunities.
With the correct, cleaned data, only then will AI go on its way to become the “Holy Grail” it has long promised it would be.
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