AI skills shortages have caused businesses to put many of their AI projects on the back-burner. A McKinsey & Company survey found that 87 per cent of respondents are experiencing skills gaps now or expect them within a few years.
The explainable use cases and real-world reach of AI is expanding, and more businesses are seeking ways to foster collaboration, gain economies of scale and accelerate their AI paths from concept to production with maturing tools. To achieve broad AI adoption across their entire organisation, businesses must start scaling their specialists’ skills and focus them on the most important tasks. AI no longer needs to be just for the small minority of machine learning experts and data scientists. With data at their core, business analysts are also eager for a slice of the pie. Until now, the everyday business analyst has been locked out of AI.
The expanding role of the business analyst
The role of the business analyst is critical to an organisation’s future and success. From breaking down complex projects to looking at ways to expand a service offering or discover a new market, business analysts play a key part in a business’ bottom line.
With new AI tools and available machine learning techniques, a company’s business analysts have the opportunity to leverage more diverse and richer data sets. With AI and ML tools at their disposal, the skills of the business analyst are expanding towards data science and their possibilities with AI are endless. Whether it’s helping to prevent customer churn, influence buyer behaviour, transform quality assurance or support brand marketing, business analysts can use AI to work smarter and achieve maximum data potential and insights in their daily work.
Preventing customer churn
Customer churn is a huge problem for businesses. Accenture reported that 52 percent of consumers have switched providers in the past year due to poor customer service, with banks, retailers, and cable and satellite television providers being the worst offenders. Churn is unavoidable in business, but when a company can minimise churn, it can achieve breakthrough results.
AI can dramatically help businesses increase customer retention and prevent churn. Business analysts should be open to moving away from traditional ways of looking at customer data–which can be manual and take too much time to retrieve insights. Analysts can use new methods of AI and deep learning that would not require any labels or annotations on the data, leveraging algorithms that find the best pattern to predict when, how and why a customer will churn.
For example, if a company has accumulated on average 1,500 data points as well as transcripts of calls for each of their customers and the business analyst wants to analyse churn, but cannot identify all of the effective variables to create a model that will provide the insights needed. AI can help forecast the proper variables and analyse the data by factors such as topic classification in customer reviews and sentiment analysts. Interpreting this data helps the business analyst understand why customer churn is occurring and the AI is able to also produce a series of recommendations to combat it.
Influencing Purchase Decisions and Buyer Behaviour
As we know, customers’ purchasing decisions can be swayed quite easily by many different factors (big and small). Basic statistics such as income, gender and knowing the current products and services used by the customer can be valuable for a business to better personalise recommendations. AI can significantly advance and personalise the output, and as it continues to evolve, it will have more of an impact on buyers’ decisions. Consumers buying in big industries like retail, banking and CPG are especially driven by predictive recommendations.
To make AI-based recommendations that help get customers and prospects to buy products, AI recommender systems can look at data points on user transaction history and user characteristics. Then, analysts can take it a step further and segment users in different clusters to recognise purchase patterns and tailor recommendations even more.
Evolving quality assurance
In today’s on-demand economy, where markets are evolving quickly, a company that can deliver quality products and services to market faster than its competitors has a significant competitive advantage. The use of AI in quality assurance produces many positive results, including an increase in the analysts’ productivity, a decrease in overall costs, early detection of high-risk areas for testing, quicker time to market, increased customer satisfaction and profitability.
For example, take a construction company that’s working on a building which is within its warranty period. Prior to using AI, the company would have relied heavily directly on people’s feedback and only a small percentage of the company’s strategy was to use drones to take video footage of the building to inspect the exterior and identify issues. The amount of time it would require to watch all the videos and identify any damage would take an army of analysts to complete. Instead, using AI and an object detection model, the company could now efficiently determine any damage that would be automatically logged as it is detected. The company could also leverage AI to spot anomalies in the use of energy across its buildings, which ultimately could help them be environmentally conscious.
The same example can be applied to a city that wants to make sure its roads are well maintained. Using video footage along with GPS coordinates from city cars equipped with cameras and trackers can collect the data while performing their everyday duties while AI software can analyse the quality of the pavement to predict when damage will happen or an urgent fix is needed. All of this can be automated and sent to a unit that is in charge of maintaining roads.
Augmenting brand marketing and reputation management
AI can deliver bigger returns on marketing campaigns. With new insights from machine learning techniques that can help better understand a campaign's target audience, marketing campaign analysts can develop more effective touchpoints for customers, prospects, and partners.
Marketers can spend their days reviewing social media sites, setting up Google Alerts, and finding other ways of collecting information, but it can be difficult to show concrete metrics and proof points in marketing. With an AI tool, campaign analysts can build a model that captures everything–from social media posts containing images or texts mentioning their brand, to the sentiment of those posts and what their audience is saying–to effectively track brand exposure and reputation, campaign success as well as insights for developing future campaigns. AI can also analyse social media posts and help a brand creative team craft the perfect post based on what kind of composition, wording, subjects in the picture drove the best engagement.
Using AI to analyse social media is just one of many marketing use cases. With AI, business analysts can also personalise content, better target advertising, make timely recommendations for pivoting campaigns, and more.
Scaling AI resources will be critical
The McKinsey & Company report found that half of respondents who expect skill gaps in the years ahead say skill building will be the most effective action for their organisations to take.
By empowering more professionals with easy-to-use AI technologies, businesses can start scaling their specialists’ skills and focus them on the most important tasks to move their organisation forward. Day-to-day AI adoption needs to extend beyond specialised data scientists and companies can support data scientists, business analysts and other critical roles with intuitive AI platforms that can help take projects from the ideation phase and into production throughout the organisation. This is an exciting time to be a business analyst.
Carolina Bessega, PhD, Chief Scientific Officer and Co-founder, Stradigi AI