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June 15, 2021 Data

Can I Use AI? How to ‘HOPP’ into AI and Help Your Business Grow

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By Brian Anderson

When we talk to prospective and existing clients about artificial intelligence (AI), the conversation tends to begin with the client saying something like, “I want to see what my team can do with a tool like Tableau.” Or, conversations start with, “Salesforce Einstein looks cool. How can my team use it?” 

We discussed in a recent blog that the biggest challenge for businesses looking to leverage AI in their growth strategy is that they want to implement it quickly but don’t have a proper database management foundation to gain value from that type of investment.  

This is a tough spot for most businesses today since McKinsey states only 1% of the World’s data is used effectively. Therefore, it is most likely that your database management is not ideal for gaining any benefits from AI. This can lead to your business making strategic decisions on inaccurate data, which I’m sure nobody in your business wants. 

At BrainSell, we aim to educate our clients on the importance of building out a blueprint that sets a strong foundation for their AI dreams to grow. Are you trying to learn how to use AI? Read on to learn more about our approach. 

How to ‘HOPP’ into AI

Instead of diving head-first into an AI implementation, BrainSell suggests formulating a game plan and then HOPP!” This acronym aims to highlight all the main areas of your database you must assess. From there, you ensure that you have a data foundation in place to make the most out of any AI or machine learning algorithm.  

If you want to use AI effectively, you must HOPP beforehand. The acronym “HOPP” stands for:  

Historical Data 

If you don’t know where you
’ve been, do you even know where you’re going?  

Your historical data set is all past information available from your company. This includes your basic financial statements, historical data about annual revenue, past engagement with campaigns, and other similar information. This data is analyzed once or twice a year to understand the company’s historical growth better.  

Operational Data 

Data fuels growing businesses by giving your teams the insights they need to move the business forward.

Your operational data set includes all the essential data your teams need to run the day-to-day initiatives of the business. This can include, but is not limited to: 

  • Engagement data from ongoing campaigns; 
  • Data highlighting the current pipeline for your sales team; or 
  • Data obtained from active support tickets. 

It would be best to gain insights from your operational data set weekly or monthly, depending on what your company can handle. 

Predictive Data 

Once you have a strong data foundation,
it’s time to focus less on what’s in the rearview mirror and begin predicting potential outcomes from various scenarios. 

Your predictive data set includes any information that helps your company’s forecasting endeavors. This can consist of:

  • Engagement data that helps identify the best ways to communicate with prospects;
  • Financial data to better forecast operating costs; or
  • Sales data to better understand sales performance or issues.

Instead of being reactionary with this data, you make forward-looking decisions to help get the business where it needs to be.  

Prescriptive Data 

After all your hard work assessing the
data mentioned above sets, it’s time to start digging into your prescriptive data to fuel AI and machine learning tools!  

The prescriptive data set is actionable insights that let you pivot on the fly. Unfortunately, this data set is the toughest to wrangle since it relies on ALL the prior data sets to uncover different signals that can benefit your business. For example, a manufacturer can track costs for various raw materials to adjust pricing for their products on the fly and based on demand. E-commerce companies can also do this by analyzing purchasing patterns to suggest discounts, sales, and more! 

Sadly, employees often overlook the uncovered signals gained from software and machine algorithms due to being inundated with data and not being able to parse out the patterns. Prescriptive ultimately combines the strategy and the data. Predictive data allows you to make forward-thinking decisions, while prescriptive data helps uncover potential scenarios to consider.  

Connect with BrainSell to Help Your Team Use AI

Ultimately, the compelling use cases around AI and machine learning are an end goal; but the initial focus should be on the business’s historical and operational data infrastructure. 

Want to learn how you can use AI in your business? Connect with a BrainSell growth enablement expert to assess how your company uses data and provide recommendations for getting the outcomes you want as soon as possible. 

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Author Bio

Brian Anderson

Brian Anderson joined BrainSell as the content marketing manager but unknowingly became our in-house troubadour as well. Brian’s ability to generate high-quality content and continue to develop the BrainSell voice is unmatched.

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