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How AI in Personalized Marketing Boosts Corporate Profitability

How AI in Personalized Marketing Boosts Corporate Profitability
Aissa Djalo
BD & Social Media

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At least for now, AI makes our lives more connected, smartphones become our right hands, our entertainment more engaging and our shopping experiences more personal. And of course, we can't wait to do all that in the backseat of a self-driving car.

But jokes aside, AI and its disciplines are becoming an unmatched weapon in transforming traditional marketing efforts into more comprehensive, appealing, and personalized customer experiences.

Marketers might ask "How AI got so big in marketing?"

The pressure comes from those 80% of customers that are more likely to purchase if they have a personalized customer experience and feel treated as an individual.

Also, 72% of shoppers that were surveyed by McKinsey see personalization as made feeling special and as investment into the relationship. The competition for attention has evolved to customer experience vs customer experience. So, benchmarking to direct competitors isn't sufficient anymore – it’s crucial that an online insurance provider is capable of the similar fast and convenient experiences as a fast-food delivery business.

based on: https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/the-value-of-getting-personalization-right-or-wrong-is-multiplying

Even though AI has changed the demand and accelerated the increasing satisfaction levels of consumers, it has also made marketing more substantiated and efficient by:

  1. Improving the Customer Experience: AI provides marketers with in-depth, transparent, and real-time data across a customer’s journey that makes targeting and post-purchase marketing more effective. Also, tailored, and real-time messaging motivate consumers to buy products they didn’t intend to and to be less price sensitive.
  2. Optimizing Costs of Customer Acquisition: AI allows businesses to free up human resources in their marketing and task them with customer needs that are considered higher priority. First through automation in data collection and treatment, but also through predictions and smarter decision making. AI can help to pre-qualify leads and better evaluate marketing campaigns

In 2020 almost 3/4 marketers have seen a 200%+increase in ROI - lets go a step further: By 2035 AI will likely increase corporate profitability by 38% on average across 15+ industries. If marketers want to accelerate their own and their client’s marketing efforts, they absolutely cannot miss out on gathering and using AI generated data. As a reader you might think “guys, who is sleeping on this subject?”, but less than 10% go beyond digital channels.

Let’s recap why AI is changing the game:

Status Quo & Outlook: How AI Improves Customer Experiences Already

AI generated data provides actionable customer insights that help with campaign optimization, highly targeted messaging and detection of when purchases will be made. To put it simply: AI improves the decisions made regarding:

  1. Who to target
  2. When to target
  3. Through what channel
  4. How to approach

Marketers can map their customers’ journey across all touchpoints enabling an omni-channel approach that results in a customized and effortless customer experience like in this example.

Our research identified the following five disruptive effects of AI on customer experiences:

[#section1]1. Better Targeting with Hyper-Segmentation[#section1]  

AI eliminates the assumptions and uncertainty in customer segmentation that remain after traditional segmentation with age, gender, socioeconomic data, location, and purchase history: AI and its biggest subset Machine Learning (ML) enabled the gathering and treatment of huge amounts of data points resulting from consumers’ overall online behavior – in real time and from multiple devices -such as:

  • Time spent on website, pages viewed, open rates of email campaigns
  • Search behavior, device, location
  • Traffic source, new vs. returning visitor, purchase history
  • Viewed, liked, or disliked content

Where AI revolutionized segmentation is in clustering this exact information into different segments that expose similarities, patterns and trends the biased and prejudicial minds of human marketers would have never detected. Also, compared to restricted human capabilities in data treatment, Machine Learning can determine unlimited numbers of segments, doesn’t need much human intervention, and updates itself automatically through autonomous learning. This enables unbiased and valid segmentation at scale at lower costs. There’s also a chance to identify growth segments that aren’t responding yet – is your heart melting already?

This leaves room for marketers to tailor their messaging based on the predefined groups to increase the ROI of ad spend. When it comes to tailored messaging, AI’s subset of Natural Language Processing (NLP) can work as a great assistant: The technology helps to understand what the target audience talks about and reacts to online which expands the insights from visible and executed actions to invisible intentions, thoughts, and feelings of consumers – all resulting in enhanced understanding of attitudes, interests, and characteristics and thus another layer of segmentation. These insights help in shaping tailored messages – not only regarding the type of content - but also in copywriting where AI tools assist in the choice of words, phrases, and tone.

Besides more effective messaging, businesses also get the chance to improve their unique value proposition (UVP) and evaluate their overall brand perception based on less biased customer opinions: Instead of relying on proactively generated customer feedback and interpretation, NLP accelerates social listening insights of when, how, and why brands are mentioned.

Taking it beyond: (Of course) Amazon patented NLP features that will help their Echo detect when an Echo-owner is ill – and you guessed it – to then suggest a whole health package that will be delivered home. Perhaps they’ll invent a recovery package for heartbreaks?  

[#section2]2. Omni-Channel Messaging for Personalized Ecosystems[#section2]

AI generated data from multiple sources allows businesses to create a branded eco-system of marketing that meets the customer at every step of their journey. A tailored and well-integrated orchestra of application, e-mail marketing, social media marketing, customer support, push notifications, and offline data makes the customer journey effortless, more relevant and leads to higher conversion rates – just because customers’ lives are made easier. What is out of stock at the point of sale, can be scanned offline and ordered online or in-app for home delivery. Here are some best-practice-orchestras of omni-channel marketing.  

Taking it beyond: Ecosystems will grow to 30% of global sales by 2025 especially through collaborations and partnerships where consumer goods, home mechanics and automotive companies will create perfect orchestras around smart homes, for example.

[#section3]3. 78% Higher Customer Retention[#section3]

Real-time data on customers uninstalling or unsubscribing apps and services, helps detecting the pain points causing these dissatisfactions but also allows us to win those customers back. The best example is Netflix, that introduced an ad-supported subscription model for a weigh lower price point in November 2022 to counteract their decreasing subscriptions – which were mainly caused by the price hikes. Guess how they tackled this issue so effectively? AI doesn’t only help in mirroring performances but also to predict behavioral patterns and thus define early warning signs.

AI-based personalization reduces customers from disengaging in general: The 2021 survey of McKinsey shows how personalization affects repurchases and how this creates the so-called flywheel of customer retention:

based on: https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/the-value-of-getting-personalization-right-or-wrong-is-multiplying#0

[#section4]4. Levels of Personalization[#section4]

Back in the day, customizing the salutation in emails was an initial form of personalized marketing after customers always used to get the same information. Manually generated data allowed first segmentations into lookalike audience where offers were further personalized. The integration of offline data from member and loyalty programs gave marketers further data about purchase behavior and interests to consider in their messaging. Later, smartphones provided locational data for geo tagging.

Today personalized marketing tends to be creepy when consumers talk about their travel plans and see a matching online ad a couple of minutes later. Ever happened to you? We admit that it’s easy to feel like someone is spying on you but when thinking about the amount of travel vlogs and related pictures watched and engaged with on social media – it's not surprising.

A recent study showed that 53% of consumers will share their personal information for enhanced customer experiences: A customer is willing to sign up for newsletters to receive relevant updates, discounts, and other free resources or preferences regarding topics, timing, and interests to receive relevant offers - this enables marketers to create individual customer journey paths that will simultaneously improve with the data provided during the customer engagement.

A more subtle form of personalized marketing is sending helpful content and product recommendations (possibly with a discount) to prospects after they searched on the businesses’ site – leading to higher probability of conversions due to the relevancy and the perceived effort of the business.

Marketing also feels intimate when a furniture business enables consumers to display its products in their own room. What else could indecisive buyers like some of us wish for? Before waiting for a delivery, walking to the POS, or dealing with return policies afterwards – the basic artificial reality technology helps consumers to finalize their buying decision virtually which saves time, increases convenience, reduces perceived risks and also has the chance for more sustainability in e-commerce.

Taking it beyond: Personalized marketing will grow in and around POS – yes back to offline spheres – where employees will use data to tailor offerings in real-time, where businesses use GPS data to send in-app offers when customers are nearby or where facial recognition will expose any self-claimed sustainable conscious consumer in the fast-fashion store.

[#section5]5. Better Prediction of Customer Behavior[#section5]

Back to Netflix: did you know that 80% of content streamed on Netflix stems from their tailored superb recommendations and only 20% is searched by users themselves? Be honest - how many good movies and series did you find on your own? We were today’s years old when we realized how rarely we touch the search bar on Netflix.

Another example is Spotify that uses AI to bless us with playlists that are acoustically related to previous songs played. This prediction of behavior results from linking historical data with specific AI solutions: What has been clicked on, what was downloaded, searched, session durations, likes and dislikes. All leading to a perfect match of messaging, target, and timing – resulting in higher engagement and individual experiences.

[#section6]Stay Tuned for Part 2[#section6]

Okay, guess we’re warmed up right now to take the AI thing a step further. This recap intensified the status quo, need and relevance for AI in personalized marketing, but what does the future hold - for example regarding AI-generated content (buzzword: ChatGPT) - and what skills must we develop?

Stay tuned for the second article to find out what skills McKinsey, KPMG, Accenture & Co suggest to overcome AI-driven challenges and how AI can be leveraged by any business size and budget!

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