Personalization has always been the holy grail of marketing, advertising, sales, and customer relationship management. So far, two approaches have been used. The most fashionable today uses statistical data to make recommendations, the second oldest but which is coming back to the fore uses coded knowledge to make these recommendations. In this blog, we will show when one is preferable to the other as well as when the two approaches can be combined in SMARTS.
Personalization can play many roles in marketing, advertising, sales, and customer relationship management such as identifying good prospects for a specific product or service, choosing a communication channel to reach prospective customers, and picking appropriate messages that fit both customer and channel.
So far, two approaches coexist. Data-based personalization and knowledge-based personalization. You may wonder which one is the best. In fact, it depends on the sector in which you are.
Data-driven personalization works well when you have a lot of data to draw insights from and when the new data doesn’t deviate too much from the old data you based your insights on. We find this case in fast-moving consumer goods sectors such as retail, as people tend to consume the same consumables every week.
On the other hand, knowledge-based personalization works well when you don’t have enough data but want to offer a product, service or content based on the knowledge you have about the prospects and your offer. We find this case in premium sectors such as luxury, wealth management, and high-touch hotels, where customer intimacy is a must.
By design, SMARTS treats both data and knowledge equally. After all, what is often called data comes from knowledge of the subject matter – It is always someone knowledgeable about the subject who labels or explains the data collected. Thus, SMARTS supports both data-based personalization and knowledge-based personalization.
The SMARTS way
SMARTS is a low-code platform that enables creating, testing, deploying, and improving automated decisions in the form of decision tables, business rules, and other representations. I will not detail it here, but you can find a brief overview of SMARTS on our blog page and a full description on our resources page. Instead, I will focus the rest of this article on how to use SMARTS for data-based personalization and knowledge-based personalization.
For data-based personalization, you can import recommendation models developed by your data scientists and leverage them in SMARTS. The models could be in Python, SPSS, SAS, or Project R among others. SMARTS integrates them if they are compliant to PMML, a standard for sharing and deploying predictive models.
SMARTS supports importing as PMML neural networks, multinomial, general, and linear/log regression, trees, support vector machines, naïve bayes, clustering, ruleset, scorecard, K-Nearest Neighbors (KNN), random forest, and other machine learning models.
There may be situations where the model must be called as an external service. SMARTS provides support for remote functions, which makes it possible to invoke the model through JSON-RPC or REST services.
RedPen. For knowledge-based personalization, you can use our rule authoring tool RedPen to write decisions in the form of rules using a use-case driven approach. A loaded data sample supplies the context for the rules and enables immediate execution and testing of each rule. RedPen mimics what subject-matter experts do when they flag decisions. When you activate RedPen, you can pin an existing rule, a field of this rule, or a rule set and change it as if you were using a real pen on real paper. You can also create new rules with RedPen, SMARTS automatically turns them into executable rules.
Pencil. You can also use Pencil, our DMN compliant graphical decision design tool for uncovering, documenting, and sharing decisions with colleagues and partners. With Pencil, you just drag and drop graphical shapes to form a complete personalization diagram. Then you add logic to the graphical shapes and let SMARTS execute it.
SparkL. Finally, you can also use SparkL, Sparkling Logic’s language for writing rules in a natural language fashion. SparkL comes with everything you need to write rules and calculations —mathematical expressions, string manipulations, regular expressions, patterns, dates, logical manipulations, constraints, and much more. You can express any imaginable personalization logic and symbolic computation, making it the choice for highly sophisticated personalization applications.
Personalization based on data and knowledge
BluePen. As said before, SMARTS treats data and knowledge equally. When you have both, you can use BluePen, our machine learning tool.
BluePen lets you explore and analyze your data using your domain knowledge to find predictors. Then, using these predictors, you can generate a model in the form of legible rules and integrate them into your decision logic.
Using BluePen, you can engineer or change the models anytime you need to. Without heavy investment in data analytics tools and efforts, you can evaluate BluePen models in simulations and quickly deploy them in the context of an operational decision.
- Personalization has always been the holy grail of marketing, advertising, sales, and customer relationship management.
- So far, two approaches have been used: data-based personalization and knowledge-based personalization.
- No one is superior, it depends on the sector in which you are: mass marketing vs. intimacy marketing.
- SMARTS treats data and knowledge equally. So, you can use it for both data-based personalization and knowledge-based personalization.
Learn more about Decision Management and Sparkling Logic’s SMARTS™ Data-Powered Decision Manager