Retail’s transformation in 2020 is not about new trends but rather it is defined by mass adoption of services that were previously offered by only selected retailers. The new purchase journey now takes four distinct paths: 1) drive and purchase – travel to a brick-and-mortar store; 2) click and pick – purchased online and pickup at the store; 3) click and deliver – purchase online and have the item delivered; and 4) subscribe and forget – subscribe online and have the item delivered a regular interval (CPG brands). Consumers have adapted to these services and clearly see trade-off between time/effort and money. The retailers that will come out on top will be those that can offer the most frictionless purchase journeys.
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
This is simple guide of ecommerce advertising solutions offered by Google and Facebook / Instagram. There is, of course, a lot more to it than the brief descriptions provided below.
SHOPS: Googles Shopping is basically a shopping channel where products are searchable but transactions occur at the brand’s website. IG/FB shops are online stores where transactions can take place within the app or at the brand’s website
PRODUCT LISTINGS: Google Shopping Ads, also known as Product Listing Ads, are single product ad units. Facebook’s equivalent are the Dynamic Product Ads.
MULTI-PRODUCT LISTINGS: Showcase Shopping Ads and Carousel Ads are Google and Facebook’s respective product ad units that allows for the listing of multiple related or unrelated products.
VIDEO ADS: Video Ads on Google can be served on YouTube or across the Google Display Network. Formats vary and can include live-streaming. FB/IG’s video ads can appear in Feed or Stories and can also include live-streaming.
DISPLAY ADS: Google’s display ads can be served across the Google Display Network. Similar to video, FB/IG’s display ads can appear in Feed or Stories as well as through the Facebook Audience Network.
SEARCH ADS: Google Search Ads are ad units that appear alongside search results based on relevant keywords. They can include ad extensions which allow the brand to include product or business details.
COLLABORATIVE ADS: Facebook Collaborative Ads allow brands that sell products through retailers and merchants to run direct sales campaigns
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.
There are many types of marketing measures that can be applied to a customer’s purchase and experience journey. However, what’s important is that these measures can both determine the effectiveness of specific marketing campaigns and predict the likelihood that a customer will move from one stage of the journey to the next.
Here is a sample of some of methodologies and/or measures that can be used in the different stages of the journey:
Market Analysis – Determine market needs
Experience Analysis – Determine prospective customer needs and/or opportunities to improve customer experience
Awareness – Determine level of brand and product awareness
Segmentation – Segment customers to the smallest groupings feasible
Addressability – Level of addressability for each segment
Media Mix Modeling – What are the optimal media channels we should use to reach our audiences
Multi Touch Attribution – How are channels performing for given audiences and campaigns
Brand Preference – What is the consumer brand preference within our category
Qualified Leads – Are we getting the desired level of marketing and sales qualified leads
Propensity to Purchase – What is the likelihood that our audience will purchase our products
Cart Abandonment – Are prospective customers dropping off before completing a purchase
Conversation Rate – What is the conversation rate for our desired actions
Time to Purchase – What is the timeframe from first touch to purchase
Up-Sell / Cross-Sell – Are we able to up-sell and/or cross-sell to our customers
Share of Wallet – What is the share of wallet
Basket Size – How much are our customers spending per purchase
Propensity to Return – Are our products being returned at unacceptable rates
Sentiment Analysis – How do customers feel about our brands, products, services and delivered experiences
Customer Effort Score – What is the level of effort required by a customer to resolve an issue
Customer Satisfaction – What is the level of customer satisfaction
Respond and Resolution Time – how much time does it take to respond to and resolve an issue
Net Promoter Score – What are our customer’s willingness to recommend
Brand Mentions – Is our brand being mentioned positively and at the right level
Revenue Referrals – How much revenue are we generating from referrals
Repurchase Rate – Are our customers returning
Frequency – What is the time between purchases within a specific time period
Recency – How many days since the last purchase
Redemption Rate – What are the number of rewards that are being redeemed
Customer Lifetime Value – What is the estimated lifetime value of our targeted segments
OVER TO YOU!
As always, your thoughts and comments are welcomed. What else could we measure at each stage of the journey? What is your approach?
Machine learning is an evolving and complex science. If one takes into account all possible scenarios, dependencies and models, it would be impossible to sketch an all-encompassing explanation. So I will focus on what the non-data scientist should know about machine learning. That is, they should focus on understanding available data, machine learning types, algorithm models and business applications.
1. First, determine if what is the nature of the available data. Do you have historical campaign data with historical results or an unstructured database of customer records? Or are you trying to make real-time decisions on streaming data?
2. The data available–and use case–will determine the appropriate machine learning approach. There are three major types: supervised, unsupervised and reinforcement learning. a) Supervised Learning allows to you to predict an outcome based on input and output data (e.g. churn). b) Unsupervised Learning allows to you categorize outcomes based on input data (e.g. segmentation). c) Reinforcement Learning allows you to react to an environment (e.g. driverless car).
3. Each machine learning type will use a number of algorithm. There are hundreds of variations. Supervised Learning typically uses regression or classification algorithms. Unsupervised Learning uses, but is not limited to, clustering algorithms. Reinforcement Learning will usually use to type of neural network algorithm.
4. The uses of machine learning are nearly limitless. Although I listed here as the last step, determining the use case or business application should probably be the first step. For example, we would use logistic regression to determine whether a house would sell at a certain price or not and we would use linear regression to predict the future price of a house. You should keep in mind that machine learning business applications typically require the use of more than one machine learning type as well as multiple algorithms.
I hope this sketch serves as a useful guide. Feel free to share and use as needed.
Thoughts and comments as well as suggestions for other sketched explanations are welcomed.
The AdTech supply chain is complex and constantly evolving. While this diagram is not all encompassing, it outlines the major components and platforms required for an effective ad strategy.
- DATA – The Data Infrastructure employs a data lake to ensure scalability, speed, and flexibility. Data integration covers all techniques to connect all varieties of structured, semi-structured, and unstructured data.
- MODELING – The Modeling layer is the environment to facilitate advanced models and analytics. These include: Marketing Mix Models, Machine learning, AI, Deep Learning, Segmentation, LTV, Attribution modeling, and many more. The key to this layer is to have the analytics environment and tools needed to generate these models (e.g. Python and R).
- PROFILING – The profiling layer is the part of the stack in which we attribute data, metrics and measurable behaviors to build segments and individual profiles (in instances where we collect this data). We can also use our data for matching or adding profiles from 3rd party vendors.
- PERSONALIZATION – This Personalization layer is the part of the stack which leverages models and data to deliver content and experiences based on behavior and preference. We use the audience information from our DMP and profile creation to store preferences and deliver against those as well.
- Why, how, who and what are we trying to solve for? Are we solving a problem or seizing an opportunity? How does it align to the consumer journey?
- Do we have the required data? Are we buying or collecting? Is it structured or unstructured? What about variety, velocity, veracity and volume of data?
- What are the appropriate statistical model? Classification, regression, clustering, similarity, etc.? What about marketing models? Segmentation, propensity, etc.?
- What is the deliverable and how does it work? Are we delivering through dashboards? Will it need to be real time?
- Why should the client move forward with the recommended solution?
- What is the investment required?
- How are we going to test and optimize our proposed solution?
How we track and measure the effectiveness of media has evolved significantly over the past 70 years. From a sole focus on reaching eyes and ears during the golden years of print, radio and TV to counting eyeballs and clicks during the 90s and 2000s to reach to measuring reach, response and relationships in today’s interconnected age.