Businesses are making changes across departments to incorporate AI into their operations and products. Beyond generative AI, the marketing department has a unique opportunity to improved awareness of customer needs and behaviours, and tailor marketing to deliver on business objectives.
Marketing increasingly depends on data and understanding the needs of your customers. Throughout AI, marketers have access to insights, predictions and decisions to streamline campaigns. Its use isn’t without potential risks however, and CMOs need to understand how AI may affect the outcome of marketing campaigns and customer trust in the brand.
The Benefits of AI for Marketing and Business
AI’s explosion into public consciousness has meant many businesses are aware of how the technology might benefit them. Frequently discussed benefits include task automation to free up employee bandwidth, faster production through generative AI, and improved customer service through chatbots and predictions.
For marketing, AI holds a much more valuable asset – data. Successful marketing relies on data to understand how customers will interact with campaigns and relate to the brand. A lot of this data was previously gathered through analytics from separate sources such as social media, or captured verbally from customer feedback or interactions. These ad hoc methods mean marketing predictions aren’t always guaranteed or likely to contribute to revenue.
With AI, marketing is less of a guessing game but about understanding the predictions and alignments between customers segments. Brands can drill down into the problems they’re trying to solve and reach conclusions that align with customers and contribute to business objectives.
Research from McKinsey reveals that companies using data-driven marketing are 23 times more likely to acquire customers and 6 times more likely to retain them. This statistic is powerful for encouraging businesses to optimise their marketing efforts using data that aligns with their customers. A similar report by Accenture found that companies using AI for real-time marketing decision-making achieve 20% higher conversion rates and 15% lower customer acquisition costs. Improving the effectiveness of marketing through AI contributes to the business’ revenue and improves customer retention.
Understanding how AI works for your Marketing
There are a range of AI tools and systems CMOs can look to utilise. These can broadly be split into two categories: Standalone programs or those integrated into systems. Where possible (with budget and resources), AI should be integrated into marketing systems to ensure continuity across customer touchpoints and collect data from interactions with Sales and Customer Service teams. Integrated systems also tend to be more subtle to the user – for example, Netflix collects data and provides recommendations within the system, rather than moving the user to a separate channel to access AI-driven recommendations.
Consideration should be made around how AI can be integrated into both internal and external channels. For example, Olay created the Olay Skin Advisor, that provides recommended products based on analysis from customer selfies, This saw an uptake in conversion and basket size, but couldn’t be replicated in physical stores or on Amazon. The adoption of AI was successful in contributing to revenue, but was not scalable for the multiple channels Olay existed on.
Gathering Data for Marketing
When AI has been built into your product or marketing system, it is time to start gathering data in order to optimise campaigns and interactions with customers.
Requests need to be carefully curated to achieve the best outcome. This requires both a marketer’s viewpoint and an understanding of the data. This Harvard Business Review article gave the example of a business launching a retention campaign. They chose to target those deemed most likely to cancel membership, which had very little impact. Had they chosen ‘swing customers’ that were still deliberating their membership renewal, the campaign likely would have had better success.
Marketers should examine the atomic or most granular level of the problem they are seeking to solve. Data analysis should be led by the needs of the customer, rather than what data is readily available. Time should also be taken to review the existing processes or campaign for that problem (if there is one), to compare action with the data.
It is worth assessing the risks or contingencies of errors in the data. Customers may exhibit behaviours that results in AI adding them to a segment, but the marketing supposedly personalised for them is irrelevant. Care should be taken to ensure AI tools learn from miscalculations and is overridden by human experience where necessary.
With AI, marketing teams are able to not only improve the effectiveness of campaigns and therefore customer advocacy, they can better demonstrate and understand marketing’s contribution to business growth and revenue.
Get in touch to understand how you can adopt AI into your marketing systems.
