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
A new business paradigm is forming, one that aligns business intelligence, creativity, big data and technology to deliver relevant connected experiences to a new generation of hyper-connected consumers. To succeed in the coming years, companies will need to fuse together a myriad of technology platforms, segregated business data, and a somewhat ineffective media landscape to reach Millennial consumers and the generations that follow.
From crawling to flying, the journey to becoming a data driven organization that can deliver connected experiences has four distinct phases. Each of these transformational phases are enabled by five core drivers: data, technology, intelligence, content and experiences.
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:
- Value: perception of the usefulness and benefit of a product compared to the price charged for it
- Quality: general level of satisfaction with the way a product works and its ability to work as intended
- Aspiration: expressing a longing or wish to own the product or to be associated with the product’s qualities
- Differentiation: the extend to which social media users draw distinctions between the qualities and characteristics of the brand and its competitors
- Presence: the size of a brand’s owned social communities weighed with the sentiment expressed by the community members toward the brand
- Influence: the ability of a brand to earn unaided mentions as well as have its messaging amplified and shared by the social media community
- Virality: the number of unique people engaged in conversations with or about the brand; weighed with the sentiment expressed by those users
- Resonance: the ability of a brand to engage users with its content and elicit reactions from them
There are countless algorithms we can used to mathematically predict an outcome to a business challenge. However, the most widely used algorithms will fall into four categories: classification, continuous, clustering and recommendation.
Let’s use a real life example to illustrate how we choose the right algorithm to solve the right problem. For illustration purposes we are making a number of assumptions to keep things simple for the non-analyst.
Let’s say that a realtor is trying to answer the following questions:
- Will a couple buy a house? Here we are looking for a categorical answer of Yes or No. For this we would use some kind of Classification algorithm, which could include: Logistic Regression, Decision Trees or Convolutional Neural Network
- How much will they pay of the house? For this question we would use Continuous estimation as we trying to determine the value in a sequence. Is this case, one would likely use a Linear Regression algorithm.
- Where will the buy the house? Clustering would be the best approach to determine where they are likely to buy a house. K-means and Affinity
- If they buy a house, what else will they buy? Recommender System Algorithms are commonly used to determine next best offer or next best action. The most commonly used Recommender algorithm is Collaborative Filtering: either user-to-user or item-to-item.
Every HR department and talent acquisition agency knows that in today’s tight labor market it is extremely difficult to find the talent that meets the organization’s business needs. In other words, the way we go about matching talent to job openings is still pretty much in the dark ages. For employers, the process is slow, expensive and labor intensive. For applicants, it is often difficult to find open positions that are a fit.
However, today we can leverage artificial intelligence (AI) and machine learning (ML) to transform the way we match talent to employers. An AI/ML solution could be built using multiple technologies but using the Google or Amazon Cloud offers cost savings, scalability and speed that few can match.
The above is a sketched architecture of what the platform could look like using the Google Cloud. I will call this solution Deep Signal.
Sample Use Cases
So, what could you do with Deep Signal? Here are a small sample of use cases:
1. Candidate Sourcing and Placement – Source candidates faster and more accurately (decrease the time from first contact to placement)
2. Source Specialty Candidates – Identify difficult to find candidates with specialized skills
3. Candidate Recommendation Engine – Recommend candidates based on look-a-like modeling (if you liked this resume, you will also like these other resumes)
4. Employer recommendations Engine – Recommend employers based on look-a-like modeling (if you are interested in this employer, you will also like these other employers)
5. Smart Search – Job search based on non-traditional features (commute time, vacation, etc.)
6. Next Best Action – Make recommendations based on actions taken by employer or candidate
7. Identify Prospective Clients – Identify prospective clients based on existing candidate database (this prospective employer will be interested in these candidates)
There are two things that will be needed in order to build a AI driven platform. The first one is data. Without extensive categorical and historical data, you cannot build accurate machine learning models. The second thing we will need is computing power. Without enough computing power, it could take weeks or months to run our models. Both Amazon and Google offer solutions with more than enough computing power to handle anything you throw at them.
Transforming into a data-driven business model based on machine learning and artificial intelligence requires more than deploying new technologies. It often requires cultural and organizational shifts as the business adapts to the realignment of data, people, process and technology.
As always, questions and thoughts are welcome. If you give me a use case and I will think of ways ML/AI could be used.