Telling a great data story begins with framing the what and the how of the questions we would like to answer. We can use our data to compare, diagnose and discover information or insights about relationships/connections, shifts over time and attributes/behaviors. However, the best answers usually are the product of a creative combination of approaches and datasets.
Headless commerce decouples back-end platforms responsible for fulfillment, operations, processes, calculations and data from front-ed platforms responsible for delivering experiences through technologies such as CMS (content management systems), DXPs (digital experience platforms), PWAs (progressive web applications) or custom solutions. One can say that the back-end is “headless” and front-end offers multiple “heads.” This is made possible through the use of a flexible API layer
Recently, I was having a conversation with a friend about strategic choices and risks in our industry. The best way to map the two is to think of them in terms of the Ansoff Matrix. Ansoff argued that business growth can only come from two sources: new products and services or/and new clients and markets.
Market Penetration – One can grow existing clients or win new clients in your current market by offering a better value propositions. These could include new pricing models, efficiencies through consolidation or lower costs through off-shoring or automation. This is considered a low risk strategy.
Product Development – One can create new products and services that complement the existing suite. These could include ecommerce analytics, identity resolution, digital/linear cross-channel programmatic or new products around content creation and management. This is considered a medium risk strategy.
Market Development – This one is straight forward. Grow business my entering new markets with existing and/or new products and services. This is considered a medium risk strategy.
Diversification – One can develop brand new products and services that are outside the current areas of expertise. This could include tech consulting, software development, ecommerce management, or in-housing solutions (within expertise but can cannibalize existing services). This is considered a high risk strategy.
Rather than using sentiment as a proxy for brand health, we should embrace a new model that measures the health of brands in the context of the competitive set and category ecosystem. The model looks at two core areas; Perception and Engagement. On the Perception side we focus on key areas that define thoughts and feelings about the brand. The Engagement side quantifies the reach and strength of the brand and its messaging. All volumes are weighed against sentiment, to ensure that brands are not rewarded for negatively driven spikes in activity. Both Perception and Engagement consist of four distinct areas of measurement:
PERCEPTION
ENGAGEMENT
Algorithmic or Probabilistic Attribution uses statistics and machine learning to determine the probability of conversion across marketing touchpoints. In other words, how much of a conversion should be attributed to each channel. In order to keep things simple, I randomly chose a few variables—out of dozens or more—that could go into our model. Let’s go through the rows one at a time.
Touchpoints – This is a mix that includes online as well as offline touchpoints.
Platforms – These are some of the platforms that can be used to collect the data for each touchpoint.
Cost – Cost is one of our most important variables since it helps us determine ROI. Just because something is effective does not meant it is efficient.
Frequency – How many times was our ad/content served to the prospect.
Action – Did the prospect take any action upon viewing our content (i.e. click on it, etc.).
Duration – What is the duration of an engagement? In this example, our prospect spent 6 seconds on landing page after clicking on a mobile ad. Also, we used technology to determine that the prospect looked at an OHH ad for 2 seconds.
Recency – When was the last engagement before conversation. The closer the recency between touch points the higher the weight to the precedent one. Here we see that the prospect conducted a product search within the hour after he/she engaged a social ad/content. Thus, “Social” would get a larger attribution.
Quality score – This a variable that you don’t see often that is extremely important. What is the quality of the ad/content? Was the ad place next to undesirable content? Was the engagement likely from a bot?
Halo – Was there a “halo effect?” That is, did the prospect take a secondary action of value to the brand. In this example, the prospect did a search for a related product and ended up buying that product in addition to the one advertised.
Data Lake / BI – This is the infrastructure needed to process the data and run the machine learning models.
Attribution – Self-explanatory.
Mapping marketing ROI should always start by defining business goals and measures of success. Marketers should then align programs, initiatives and strategies to the theses goals—with each category having its own benchmarks and goals. These measures should fall into two categories: measures of success and leading indicators to these measures of success.
When possible, brands should use simulation to forecast the ROI of marketing programs and initiatives. The above is a sketch/draft of a loyalty program simulator I crafted for one of the largest home furnishings brands in the world.
Although it is important that brands define customer relationships through the lens of the consumer journey, it is helpful sometimes to think of funnels when trying to optimize customer acquisition. Brands lacking a strong acquisition strategy tend to fall into one of four funnel types: Transactional, Viral, Salesforce, Bottleneck.
Transactional – Typically commodity, price-inelastic brands with focus on a low cost product or service but not a quality product nor user experience.
Viral – A popular product or service where the brand is unprepared to handle the high demand. Products are often out of stock or ecommerce website crashes because it cannot handle volume. Customer support also falls short.
Salesforce – Typically a B2B brands where transactions are driven by its salesforce but lacks both a strong marketing strategy or product/customer support.
Bottleneck – This is the opposite of the Salesforce Funnel. Where marketing and customer support are very strong but the salesforce infrastructure cannot handle the volume of transactions.
