With the quality of CRM data being the main factor for AI success for sales teams, smart data enrichment transforms from a "nice to have" to a "must-have" for every organization aiming to implement a successful AI + CRM program.

We were all excited to hear in the recent Salesforce World Tour about the new announcements of integrating CRM data with GPT. Generative AI is here, full of promises to supercharge your sales team, giving them the power to find new prospects, predict their needs, and close deals faster than ever. That's where data-centric AI comes in. It aims to revolutionize sales productivity, but there's a catch: data quality is everything.

Data-centric AI hinges on one fundamental truth: AI is only as good as the data you feed it. Imagine your sales team armed with accurate, updated, and complete data about each customer's preferences, behavior, and needs. The possibilities are endless. But data hygiene is a common challenge for sales teams, with 30% of the data becoming irrelevant after just 12 months, according to Gartner, and 47% of enterprises state that they cannot trust their CRM data, according to Forrester.

Keeping the CRM data clean is not a new challenge. Salesforce Admins traditionally find themselves running after sales reps and asking them again and again to update their records. Sales reps, on the other side, are trapped in never-ending admin work that consumes about 70% of their time, leaving them only 30% for actual selling. But data quality and accuracy are now more critical than ever. Because this is the foundation of ‘data-centric AI’ and is predicated on the notion that AI systems are developed using only quality data.

So how can sales departments adopt the ‘data-centric AI’ mindset without adding manual admin work to their sales teams? Welcome to the world of smart data enrichment – the true enabler of data-centric AI for sales success.

Smart data enrichment is the secret sauce that elevates the effectiveness of data-centric AI in sales. By tapping into the wealth of information available on LinkedIn and enriching Salesforce records, sales teams can ensure high-quality data while minimizing manual copy-pasting, tab switching, or manual typing work. By integrating Salesforce with other data sources, such as LinkedIn, sales teams can enrich their accounts, contacts, and leads in two levels: single record enrichment or mass data enrichment.

How can we make it happen? According to a recent article published by Salesforce, data quality can be gained by these four steps:

1. Remove Duplicate or Irrelevant Observations: Combining data sets from various sources can lead to duplicate entries or irrelevant data points. Cleaning the data involves eliminating these duplicates and focusing on relevant observations to enhance the efficiency and accuracy of AI analysis.
   For example, having two records of the same contact with two different titles can be solved easily using smart data enrichment, which brings the most updated title from LinkedIn and suggests which record needs to be updated or removed.
2. Fix Structural Errors: Data may contain typos, incorrect capitalization, or mislabelings. Consistency in data entries is vital to ensure accurate and complete analysis by AI systems.
   Using smart data enrichment, you can enrich the account or contact data in the same way they describe themselves on LinkedIn and approach them consistently, using this data as a single source of truth.
3. Filter Unwanted Outliers: Outliers can impact data analysis, either indicating incorrect data entries or providing valuable insights. Proper analysis is required to determine the validity of outliers. With mass data enrichment, validating outliers can be done easily.
   For example, imagine that in a single account record, the sales rep accidentally put the wrong value in ‘company tech stack.’ Bringing this data from LinkedIn and cross-correlating it with the manual input can tell whether it was a human mistake or an outlier that needs to be considered during analysis.
4. Handle Missing Data: Missing or incomplete data is a common issue that affects the accuracy of AI models. Companies can deal with this problem by eliminating observations with missing values, inputting missing values based on other observations, or altering data usage to navigate missing values effectively.
   Mass data enrichment is an ideal solution to review thousands of records and automatically add the missing data using trustworthy sources. For example, adding a business email for every contact where only private email appears or no email appears at all.

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Conclusion

Smart data enrichment, driven by the integration of LinkedIn and Salesforce, is the key to unlocking the true potential of data-centric AI in sales. By removing duplicate leads, fixing structural errors, identifying decision-makers, and personalizing interactions, sales teams can maximize productivity and efficiency. Embracing data-centric AI allows sales reps to gain valuable insights, supercharge customer interactions, and close deals with confidence. In the fast-paced world of sales, the strategic utilization of AI and smart data enrichment is not just an option; it's a competitive advantage.

Sales teams that embrace this transformative technology will stay ahead of the curve, leaving their competitors in the dust. So, are you ready to propel your sales productivity to new heights with data-centric AI and smart data enrichment? The power is in your hands.

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