How Small Businesses Can Use Machine Learning to Power Digital Transformation

If you are a small business owner you are probably thinking if you should start taking advantage of machine learning or if it’s something for bigger companies. ZDNet has the answer!

Businesses should start small and fail fast with machine learning projects to get the best return of investment. Some of the most common use cases for SMBs include fraud detection, sales optimization, marketing, and document analysis. But the benefits of implementing machine learning go even further, it can help reduce costs, drive efficiencies, increase speed to market and it can help anticipate the short-term and long-term impact on sales and adjust the strategy accordingly.  

Three examples of how SMBs are powering their digital transformation

1. Marketing

Kristina Conely, director of data and analytics at AIM Consulting, said AIM worked with a hotel company to improve its marketing programs using machine learning. At the start of the project the company had no centralized data repository, and all the reporting was manual. The first goal was to analyze products and services sold in a particular region and look for new marketing pitches. The company owns hotels and wanted to automate the process of recommending upgrades and add-on experiences for guests. To accomplish this, AIM helped the company create multiple machine learning models.

Conely said she and the client’s marketing team refined the algorithm over the course of three weeks to make sure the team trusted the results.

“At first the response was: ‘These two things will never sell together.’ But as they saw results they came on board,” she said. “They also realized that now they had time to do the analyst-type role they were brought on for.”

Part of the process of implementing ML is helping clients understand how to create and modify the algorithms.

2. Contract analysis

Another ML project that’s a good fit for small- and medium-sized businesses is contract analysis. AIM Consulting worked with a small law firm that needed help identifying which cases had the highest likelihood of a successful outcome. The company has a small team that spends a significant amount of time reviewing past cases to make these decisions. 

AIM used natural language processing (NLP) to read the historical documentation and legal outcomes, compared that information to the potential case and then derived a scoring mechanism. Using NLP dramatically shortens the research time and decision-making process.

3. Contract management

Another good use case for ML is contract management, specifically automating the signing process. Software company Conga helps businesses automate contract lifecycle management (CLM) including the need for multiple signatures on a paper document. The platform allows Salesforce users to manage contracts directly in the application while automating CLM from creation to signature. The software also automates reporting, tracking, and reminders.

Conga’s Digital Transformation Officer, Aishling Finnegan said that the best approach to using ML is to map technology to a company’s existing processes and build an individualized road map for digital transformation.
 
“If you have a more programmatic approach, you’re more in control, and it feels less overwhelming,” she said, adding that demos of AI software are often too complicated.

Finnegan said that automating the contract process is especially important now that entire companies are working remotely.

“Sales teams are able to generate vital important documents at home and get them to clients quickly,” she said. 

Automating and analyzing the sales process can spot holes in the pipeline, Finnegan added.
 
“This helps you know where you are in the customer life cycle, and it can automate triggers and reminders without anybody touching it,” she said. “This gives the business visibility, and if something has stopped, you can figure out if it’s your salespeople or the customer.”

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