How can companies become large, profitable and efficient and at the same time treat their customers as individuals, provide personalized services, and understand their needs on a granular level?
There is a fundamental paradox that will hit nearly every company at one time or another. On the one hand, there is an imperative to grow. To become as large, as efficient, and as profitable as possible. But on the other, to be good at what they do, companies have to treat their customers as individuals, provide personalized services, and understand their needs on a granular level. It then comes as no surprise to discover that the first is frequently at odds with the second.
As they grow, companies accrue more and more data in all sorts of forms. Images, product information, customer data, transaction history, compliance data – the list goes on. At the same time, the number of customers and therefore market segments the business needs to serve also expands, making offering the personal touch in even the most basic promotional communication an increasingly unlikely pipe dream.
With standard content management techniques and technologies, it’s simply not possible to address a large and diverse customer base on an individual level, either efficiently or accurately, or even consistently. To keep pace with growth, something has to give, and so the temptation is to start targeting customers using ever broader segmentation, relying on generic communications to get the job done.
But customers don’t appreciate being treated as a single, homogenous group. Research from Econsultancy suggests that 63% of consumers expect personalization as standard, and further research from Emarsys suggests that 41% of consumers wouldn’t buy from a brand again if they received carelessly targeted marketing in return. Fans of the ‘spray and pray’ approach are also wasting their time, as its also reported that 66% would ignore all marketing from a brand that couldn’t get its targeting right.
Add artificial intelligence (AI) into the mix, however, and this doesn’t have to be the case. While the letters AI tend to conjure up imaginings of some kind of dark future where the machines have taken over and are making all the decisions, in this context it’s probably more helpful to switch the two letters around round and think about it in terms of I and A – intelligent automation. Companies open to making use of the AI capabilities in its content management tech can cope with the challenges brought by growth, for example sorting through the incessant deluge of information, organizing and directing where needed, all at a scale and speed that wouldn’t be possible otherwise.
Take visual assets for example. A vital marketing resource for most companies, managing image libraries is an intensely time consuming and costly process, and one which is open to error due to its scope. The duplication of image assets can be responsible for budget wastage, as companies over pay on royalties only to hold several identical versions of an image for no good reason, or worse, the holding images of without having the rights to use them, with no overview of copyright issues or other permissions regarding content such as customer images.
But AI doesn’t just save companies from unnecessary cost and trouble. It also has the potential to grow revenues. Thinking back to that need for personalization, AI can help the marketing department identify the most appropriate images for marketing collateral, even when those communications are deeply segmented.
Using such intelligent automation, it is possible to select an image linked even to a single customer, relevant to their latest interaction with the company or even broader behavior (searching the internet for holidays in Croatia, for example) and deliver a targeted message accordingly. And this approach could be replicated for hundreds of thousands of other customers, all in real or near to real time. The results of personalization at scale can be impressive - Econsultancy reports that a 2017 study from Customer Data Platform, Segment, found 40% of US customers have bought something more expensive than they planned to because their experience was personalized, while a further 44% would probably buy again from a company that personalized their experience.
And the same techniques apply to text assets, which can be sorted, stored, and accessed according to individual need but also at scale. Information can be tagged, templated, distributed, and even translated for new markets at far greater speed and accuracy than if the same tasks were attempted manually.
This is just a snapshot of what might be achieved by integrating AI into a company’s content management strategy. This censhare whitepaper explores n more detail the topic of how AI and ML capabilities are being used in content management technology and strategies today, and is available for download here: Thinking About Content: Artificial Intelligence And Machine Learning In Content Management.
censhare relaunches the brand to reflect its content leadership in the MarTech landscape.