Data, Data Everywhere – But How Do You Know Your AI Model is Getting the Right Data?

Data may be created equally, but not all data is equal. B2B organizations that seek customers for their goods and services need to develop methods that will enable them to “discriminate” among the data that enters their AI models – in order to ensure that those models provide the insights and information that they need to accomplish their goals. To do that, they should concentrate on building models that draw as much as possible on their own, proprietary data – the data they gather from communications with clients, sales and marketing reports, responses to campaigns, and dozens of other metrics.

While traditional outreach, marketing, and sales strategies work just fine, organizations seeking to get a leg up on the competition are increasingly turning to AI. With a good AI model of their customers and market, companies can design far more effective marketing and sales plans and efforts – because AI algorithms can far more efficiently and quickly analyze the thousands of data points that will help organizations develop more effective strategies.

Data quality – data that truly reflects an organization’s markets and potential customer base – is the key ingredient here. With the right data, companies can nimbly and efficiently develop effective marketing strategies, determine which markets to concentrate their efforts on, and build potent strategies to reach the most qualified customers. “Bad” data, on the other hand, won’t help organizations accomplish those goals – and in fact may be responsible for huge losses.

While ensuring data quality is crucial for any organization using AI models, it’s especially important for companies that are new to AI – companies that are struggling to implement AI models, gathering data from public and proprietary sources. What sources should they be utilizing? How do they determine that the data they are getting will help them develop the most effective model? How do they ferret out the useful data from the non-useful? Given that as many as 85% of AI projects fail – many of them due to poor data – these are questions that organizations need to take very seriously before embarking on their AI journey.

There are several paths an organization can take to populate their AI model with data, among them contracting with a firm that will supply data from large public and proprietary databases about the industry, potential clients, competitors, trends, and more; basically filling up the model with data provided by these firms, enabling organizations to quickly move forward with AI. It’s tempting, but for many organizations it’s likely to be a mistake; while much of the data supplied by these firms is likely to be useful, there will probably be enough inaccurate data to skew the AI model with data that is irrelevant, or worse, detrimental to organizational goals. In addition, sharing an AI model with a third party could constitute a security risk.

A better path for organizations might be to rely on external sources for “big picture” industry and economy data – but to utilize their own internal, first-party data for specifics on customers, their specific markets, their competitors, and more. Such data reflects the exact market and customer base an organization seeks to reach – because it’s based on data sourced from interactions with exactly those customers. Even young organizations have more data than they realize; e-mail messages, phone calls, instant messaging data, and other communications can be mined for information about markets, customers, trends, the financial state of customers, buying patterns, preferences, and much more. By basing their models on that data, organizations can help increase the accuracy of their AI algorithms.

Organizational CRM systems can yield valuable data, with every transaction, successful or otherwise, evaluated for indications on how customers relate to products and services, which approaches (messaging, e-mail, phone, etc.) are most likely to succeed, what customers liked or didn’t like about the organization’s products/marketing/approach, and much more. That data is analyzed by advanced algorithms to determine the best way to reach potential customers and markets; what they are most likely to respond to, such as messages about quality or cost reduction; what outreach method (e-mail, phone call) they are most likely to respond to; which decision-makers are most likely to respond positively; and much more.

Phone calls, for example, can be analyzed for things like customer sentiment, keywords, indications of the future client plans, reactions to proposals, excitement relating to specific ideas or proposals, overall interest (based on, among other things, the length of a call), and more. E-mail, social media messages, website interactions, trade show and event meetings, and any other method the organization uses to reach out to clients can be similarly analyzed. The result is a trove of the most accurate and relevant data possible – since it comes from the organization’s customers and markets.

After building this highly accurate basis, the organization can beef up the scope of its model utilizing outside data sources, which the AI system’s algorithms and agents will check against the baseline data. If the third-party data is compatible with the included data on the organization’s customers, markets, goals, economic conditions, and overall strategy, that data can be included in the model, further enhancing its effectiveness. If that data doesn’t match or support the CRM-derived data already in the organization’s possession – the data about its actual customers and markets – it gets rejected, and the AI model retains its integrity.

It’s an effective strategy for all organizations – and maybe even more so for small or new organizations, who can utilize their CRM and customer data to build an effective AI model at the outset, without having to weed out legacy data that may no longer be relevant to an organization’s goals. And with that smaller but more agile model, organizations can much more quickly and efficiently determine how effective their AI efforts are; if the response rate to their campaigns and efforts isn’t as robust as they expected, they can use their AI system to quickly determine the tweaks they might need to make.

Done right, AI systems can save organizations time, money, and effort – helping them design and develop campaigns, approaches, pitches, research, and outreach that will enable them to clearly communicate what they do and why clients should do business with them. AI can help organizations ensure that their messages are aimed directly at the highest-value potential clients who are most likely to be interested in what they are offering. And, AI can help an organization quickly pivot or expand into new markets, ensuring they are taking full advantage of their potential. But the magic of AI is built on the quality of the data the algorithms use – and by sticking as closely as possible to their “home-grown” data, organizations will be able to build the most effective AI data model possible.