Businesses have reached an era of individual service for customers, and for good reason. Consumers have an unprecedented amount of information at their fingertips — from price to quality to service — and as a result, getting customer conversions is more challenging than ever. Forrester estimates that every $100 spent on getting web traffic to an e-commerce site results in only $1 of conversions.

Customer interaction needs to be purposeful, not driven by tactics like spamming them to death after they’ve purchased an item. In the global world of e-commerce, only the savviest companies will dodge the dreaded “unsubscribe” button and maintain a profitable relationship with customers.

Delivering on personalisation — something 83% of customers expect — is a more relevant strategy, creating meaningful interactions that deliver a higher return on investment. This has proven true even for companies that already have the lion’s share of their market’s dollars. Coca-Cola’s millennial-minded “Share a Coke” campaign, featuring individual’s names on its bottles, has boosted consumption of the product by a whopping 200 million bottles per day.

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So, what does this have to do with artificial intelligence (AI)? Machine learning and predictive analytics are ushering in a new era of business intelligence, allowing organizations to define the behavioural paths of individual customers. With AI, each customer touch point is not only documented and measurable in real time, the process of analyzing it can also be automated, allowing for a higher quality and quantity of business intelligence insights than ever before.

The effects of personalised marketing funnelled through AI have already been massive for the companies at the leading edge of the movement. When Amazon started uncannily recommending products its customers might like, it had a 29% increase in sales. When Netflix created a model to predict which movies its customers wanted to watch, the company saw those recommendations winning out 75% of the time.

Fortunately, AI that affects the consumer’s experience doesn’t have to be relegated to top-tier, billion-dollar ventures. Companies of all sizes can leverage AI to create high-impact business outcomes, even going so far as predicting future profit or customer behavior, by applying them to a few key metrics.

Conversion rates

Traditionally, businesses that wanted to solve their conversion rate issues turned to methods like A/B or multivariate testing. This process often takes a few months after initial setup to determine a path ahead, based on user behaviour over time. This wait-for-the-data-to-come approach isn’t fast enough for modern businesses.

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Today, AI presents an alternative. Businesses can track which visitors turn into customers or qualified leads in real time. Instead of developing a hypothesis regarding individual factors for customers that aren’t converting — like poor website navigation, complex forms to fill out or confusing calls-to-action — AI can track all these factors at once. With this blended approach, AI can combine different tests and getting a holistic reading on what’s deterring prospects. It allows for the testing of a huge number of ideas in a short amount of time ensuring companies don’t waste precious time sleuthing down the wrong metric.

Customer churn

Alternately, businesses can tell which customers are about to “bounce” by applying AI to churn data. After training a machine learning model with some historical information, AI can separate customers that are likely to leave from those that are likely to stay.

To leverage this approach, companies must first clean up their datasets, ensuring they include all relevant key performance indicators (KPIs) such as account length, location, usage and, of course, churn. As with any model, companies will inevitably need to tweak the initial parameters, sorting out any prediction errors. Once this process is perfected, signs of churn will become more apparent.

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Then, AI can empower marketers to take a proactive approach within their business process and provide targeted opportunities where these users spend their time. They can train their churn algorithm so these users gain confidence in the brand over time. This process has an end goal in mind — use automation to provide deals and perks to customers identified as likely to churn. While companies will never get to a zero percent churn rate, they can get down to a science which customers need a little more attention or a slight discount to retain that relationships.

Customer satisfaction

Customer satisfaction is the hallmark data point to supplying a top-notch customer journey. It’s these patrons that will keep coming back to your company, leave five-star online reviews, and spread the word to friends and family.

Unfortunately, many companies refer customers to surveys to get this data, and what is more punishing to your best clients than overloading them with forms? Instead, companies offer deals in exchange for customer data monitoring. Using an opt-in approach creates transparency, and then data like website clicks, social posts, mobile device use, shopping cart behaviour and so on can be used to measure this KPI.

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Additionally, natural language processing has grown to be one of the most sophisticated applications today. Instead of relying on clicks counts, companies can use AI to interpret sentiment through social media networks.

Marketers can use an algorithm that aggregates how its brand is referenced in real time, parsing positive promotions from complaints. Companies can blend this powerful information with other KPIs to see what business decisions result in positive or negative sentiment.

Get personal with AI

The reality of customer service today is the more personalised, the better. There’s too much data out there on customers to not make each buyer’s experience unique, and to do that effectively, companies must use AI.

Companies that adopt AI and the technologies that fall within are setting themselves up to outpace their competition and provide the personalised experience that keeps customers coming back for more.

Sourced by Roman Stanek, CEO and co-founder at GoodData

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