Relevant, personalized content can significantly improve outcomes and improve revenue—some organizations have reported uplift of 70% for products that contain meaningfully enriched content. Personalization can dramatically improve opens, click through rates, and ultimately conversions and revenue.
Yet frequently, during the development of a personalization plan, the rules for determining exactly what piece of content should be delivered to end users are described as “to be determined.” The fact of the matter is that although personalization is given a great deal of lip service by technology vendors, gurus, strategists and other specialists in the industry, it is a very tough problem to solve.
A common obstacle is that information about the user is not sufficiently detailed to develop effective personalization rules. Stating that the “user must be able to locate information needed in their job” is too vague to drive meaningful functionality. We have to ask what information, accomplishing what task and whether there are additional inputs required. This process allows the designer to build out a bigger picture of what users are trying to accomplish, and begins to provide clues about how they solve problems and go about tasks. Each insight should add more context, allowing anticipation of what users will require at the next step as they go about trying to achieve their aim.
Here are some considerations for choosing an approach and having the correct expectations for how the site will function.
Why is the organization working on content personalization? This may seem like an obvious question with a trivial answer “to better meet the needs of its user” or “to make information easier to use”; however, there are nuances to the objectives that will impact the approach. A B2B company that is attempting to better serve the needs of its smaller customers will take a different approach from one that wants to personalize its employees’ experience on the intranet; an ecommerce site that wants to make suggestions for its shoppers depending on what they have put in the cart will take yet another approach.
Three approaches to personalization are:
Content segmentation – Selecting in advance the content relevant to a certain type of customer
Manual personalization – Focusing the content on a particular sub-set of the users, depending on their roles (passive personalization), or allowing them to select areas of interest (active personalization).
Dynamic personalization – Modifying the selections presented depending on user choices during the interactions or based on past behaviors.
Each of these approaches is dependent upon research about the user, and requires an understanding of scenarios, tasks, goals, and journeys along with the information and content required at each step of the process.
Additional details about the user and what they are doing provide the signals that can be used to anticipate the things they want. A sales person who is on their way to visit a client, and searches for “product specifications” will need different information as compared to an engineer using the same search term. Content can be personalized with this in mind, but it needs to be organized and tagged in such a way that the system can present information based on the user’s identity and goal. This mechanism is enabled through attribute models.
Because user context varies, the more we can describe it, the more understanding we will have about user needs. Users can be described by multiple attributes, each of which provides a signal about their context and requirements. Those attributes can be based on objective characteristics, such as age and demographic or they can be more subjective, such as the type of buyer they are. Marketers typically bundle a number of attributes together and define a “persona” based on the characteristics that are important and that can be treated differently depending on their merchandizing strategy.
Content can be presented in a way that allows the user to select their path through a body of content. One challenge is selecting labeling and developing navigation that the user identifies with. Do they consider themselves a business oriented user or a technology oriented user or are they both? Are they a home user or business user? The choices should be clear and unambiguous. Content based on site behavior, past history, interests, or demographic details will be effective only if that content is clearly more relevant for users who meet those criteria.
Machine learning uses a variety of techniques to identify patterns in information. The system “learns” because over time, the results data are fed back into the system to allow adjustments to the algorithm. Machine learning algorithms are used in search and in predictive analytics. This relates to personalization because personalization is about making a guess about what someone will need based on certain signals or inputs. In the case of search, that signal is the search term. As we add more signals (location, history, etc.) the system can make more educated guesses about what they need.
Some e commerce providers use simple measures such as recency, frequency, and monetary (RFM) to segment high-value customers and present specific offers. Rules typically tie into marketing and merchandizing strategies, sales strategies, time of year, inventory details, branding decisions and overall market positioning. These rules can also be combined with other data sources to provide a more personalized interaction – such as using the customer’s name in email messages or referring to a recent purchase category or product. A simple personalization rule might be to include a link to content that describes new ways of using a recently purchased product or how to get the most from a purchase.
Developing personalization rules, structures and processes for large amounts of content and diverse users is a significant challenge for many organizations. Content and product information managers at organizations attempting to do this at scale frequently become overwhelmed with the volume and complexity of rules as well as the workflow and governance challenges needed to keep these processes functioning.
In order to ensure that personalization strategies are effective and impactful, customer engagement throughout the journey needs to be measured and analyzed. By analyzing and measuring customer behaviors, the most appropriate personalization approaches can be refined over time. Metrics drive content management and governance processes to allow for continual updates of meaningful, highly engaging content. Ongoing product and content curation and personalization leads to greater levels of engagement, which in turn increases customer loyalty, builds stronger relationships, and ultimately leads to higher sales and greater market share.
Personalization and contextualization is the differentiator in a marketplace that offers pricing transparency and similar product selections. Building expertise and process maturity in these areas will be the new mandate for both B2C and B2B organizations.