Ein Artikel von Erik Obermeier, ehemaliger Mitarbeiter DYMATRIX
Marketers are responsible for a wide area of key processes of the Customer Buying Journey. It ranges from Discovery, Evaluation, Purchase and Post-Purchase. I.e. planning, budgeting, operational challenges to Performance. Customers do a lot of decision-making before they will be in touch with a sales person, and it’s the marketer’s responsibility to engage customers during this pre-purchase stage. Being focused on our customer translates into “know your customer and deliver relevant communication”. To know your customer equates to being data-driven, i.e. leverage your data for the most appropriate and successful communication with your customer. Data is the key to improve the Customer Buying Journey, no matter which metric you are applying.
Marketing Suites from a wide range of technology vendors are promising the Holy Grail to the marketer. Marketing Suites talking about best-of-breed solutions brought together and being perfectly integrated into one Suite that serves all your goals. Many have one thing in common: they tell us the data piece is easily resolved for the marketer.
All „Marketing Suite Projects“ have had one thing in common: data was the one topic, which was not easily resolved respectively is the key to deliver customer insights that propel the Business. The more mature a marketing team becomes the more insights and detail it requests. You are in need for experienced resources, ideally understanding your industry to pull out all the insights from your data to deliver relevant communication to the customer.
You require tools, which boost your productivity by making your Data Driven Marketing Automation repeatable in every step of the process.
Let’s check out an example: “Couponing optimization based on uplift modeling”
Every retail business is super excited about this subject: Whom (customer segment, value) do I send which coupon (value) when (time) and where (location) to drive additional revenue.
- Do you apply a simple difference score model, a regression-based uplift model or a tree-based uplift model?
- I.e. based on models from Victor Lo, 2002 or Radcliff/Surry 2011, Rzepakowski/Jaroszewicz 2012?
- How will we ensure that the model quality remains above our threshold? How many resources do we require for 20 models, 50 models, 100 models?
- Will we be able to easily apply the rules in our Customer Buying Journey?
Which Marketing Suite delivers the answer to these questions?
The key to understand your customers and being enabled to easily apply these insights is the Customer Data Platform. Your communication channels (email, mobile, text, social, POS, TV, etc.) should not determine your data analysis layer, aka customer intelligence layer: it would possibly limit your data analysis capabilities to the communication channel. The marketer determines the channel based on detailed data analysis and chooses the most applicable vendor per channel: AUTOMATICALLY based on DATA insights.
Therefore, a Marketing Suite should deliver at least this:
Does your Marketing Suite deliver a ONE Customer Data Platform? Does it apply Web Analytics, Social Analytics, DataMining models on this ONE platform, i.e. leveraging purchase, historic data and realtime information to deliver the relevant NEXT BEST OFFER to your customer?
Let’s study examples in my forthcoming posts…..