The $410 billion E-commerce sales are only 9% of all retail sales in the U.S. and it keeps increasing by a whopping 14% YoY. It is predicted to grow to $600 billion by 2020. Perhaps you may not have seen a 32% growth in your e-commerce revenues like Amazon did last year, but did you at least hit the 14% average? If not, here are a few possible reasons why and how Artificial Intelligence can help.
You probably have thousands of products, a website with the latest e-commerce features, a mobile app. You may also have invested millions in brand awareness, user acquisition, and user behavior analysis but did not see any significant lift in your top-line.
When it comes to marketing funnels there isn’t really a magic formula; the success comes by doing small things in a big way. There are countless aspects like customer service, return policies, deals, customer reviews, seller reviews, autocomplete suggestions, finding items, displaying customer reviews, product promotions, price comparison, aggregated listings, rich product information, discounted shipping and much more which you need to get right.
In fact, take a look at the solutions of the top e-commerce retailers today. Their product offerings, websites, and apps are very similar on the surface yet Amazon has 43.5% market share compared to 6.8% of its closest competitor eBay: (source Recode )
- 43.5% Amazon
- 6.8% eBay
- 3.6% Apple
- 3.6% Walmart
- 1.5% Homedepot
- 1.4% Best Buy
- 1.2% Macy’s
- 0.9% Wayfair
- 0.9% Costco
But, what are these small differences between solutions and how can you improve upon them? Let’s take a very simple feature like “search autocomplete suggestions”, and see how this technology differs. Below are the results of “search autocomplete suggestions” feature for the “Desk lamp” keyword on three different websites:
One easy-to-spot difference is that Wayfair is expanding the query by prefixing categories, whereas Amazon and Walmart are using some kind of intelligence. At a very high level, the intelligence seems to be a combination of past public query patterns and maybe some additional logic. Since “Desk lamp white” is not even a correct English sentence I would say there is a flavor of similarity based on language understanding. The “Desk lamp for an office in home” top suggestion of Wallmart is a very niche category and isn’t anything I’m looking for. It is also surprising that neither of the solutions seems to have personalization built-in; regardless of the account, the autosuggest results are the same. It is possible to fix the irrelevant suggestions with a personalization solution which includes user’s own query and purchase history.
There are also many more data points we can associate with a search term to make it more intelligent. These include:
1) the quality and quantity of the product data related to the search term,
2) the cumulative product review scores related to the search term,
3) the cumulative product sales volumes related to the search term.
In summary, optimization objective of a “search autocomplete suggestions” is maximizing sales and that requires finding the balance between many attributes.
Another example of intelligent features on Amazon is its real-time price comparison. We know that Amazon does not always have the lowest prices but it has the intelligence to fine-tune the prices in real-time such that it will produce wider margins on less competitive items and private-label goods where the user is less likely to compare prices.
This kind of intelligence requires vasts amount of data extracted from pricing, customer reviews, product rank, customer-product behavior, collaborative aspects etc. and a vast amount of processing power to support the volume of 183 million monthly active users. This is where artificial intelligent solutions come into the picture.
In addition to Amazon, many large tech companies like Apple, Google, Facebook, Intel, and Microsoft have invested decades into artificial intelligence and now they are riding the wave by reaping the benefits. Currently, the reason that the sales gap between Amazon and its competitors is growing is due to Amazon’s ability to collect vast amounts of data and use artificial intelligence to understand it and then feed it back to the marketing and sales channels. Hopefully, this is not a complete surprise to you but perhaps you feel constrained in jumping on the artificial intelligence bandwagon due to constraints such as domain expertise, resources, and costs.
According to Stanford University’s 2017 AI Index Report between the years 2000 and 2017 the number of AI papers increased 8 fold, annual VC investments into AI startups increased 6 fold and the number of startups increased 14 fold. In 2017 alone a record $6.5B of capital was deployed across 650+ deals, surpassing all 2016 numbers.
As with any new technology, especially at this scale, we see more questions than answers. The most common question is “how can I move my company to the AI era?”. One of the reasons for the abundance of questions is the lack of AI expertise in the market. The demand for AI experts is high but there are not enough of them out there. The AI Index report sheds light on this expertise shortage and shows that Stanford had only 1500 AI/ML course enrollments in the last year. Comparing this number to 3.8 million software engineers in the US it seems like it will take quite a bit of time to have AI skills commonly available. Also, you will be competing with the large tech companies like Apple, Google, Amazon, Facebook, Intel, Microsoft and others for such talent. Which brings us actually to another shortage, that of AI product and service companies. AI is trending and this has affected the start-up ecosystem as well. According to CBInsight in 2017 alone, over 55 private AI companies were acquired, mainly by the most active large tech companies Google, Apple, Facebook, Intel, Microsoft, and Amazon. Until the large companies are saturated with enough AI startups we will have a shortage.
So, what should you do?
Luckily, even if you don’t have an AI lab with hundreds of experts with PhDs, most of the AI components have now become mainstream cloud technologies and you can work with an AI integrator to speed up your integration. Today, e-commerce solutions like the ones below have become readily available to you at affordable prices:
- Personalized search
- Optimized rankings
- Email recommendations
- Behavioral triggers
- Smart content personalization
- Content sequencing
- Product recommendations
- Personalized engagement
- Persistent visitor intelligence
- Actionable insights
- Conversion rate optimization
- Omnichannel marketing
- Price Optimization
- Product insight
Ironically, you can compete with Amazon by using another Amazon product, namely Amazon Web Services (AWS). Besides its broad solution space, AWS has a set of AI solutions which can be combined and integrated easily in a matter of weeks. Going back to our example, AWS even has a commercial“search autocomplete suggestions” solution you can integrate into your system.
As a final note, I want to share with you a list of almost all Amazon AI services ready for integration as of today (aws.amazon.com). Of course, as much as there are other cloud AI service providers there is also a great deal of AI technologies more on the horizon to come.
Amazon Rekognition Solutions
- Image Object detection
- Image Face detection, search, tracking, compare
- Image Celebrity detection
- Image Unsafe content detection
- Video Object detection
- Video Face detection, search, tracking
- Video Celebrity detection
- Video Unsafe content detection
- Video Person detection
Amazon Comprehend Solutions
- Key phrases extraction
- Sentiment Analysis
- Entity Recognition: places, people, brands, events
- Language Detection
- Topic Modeling
Amazon Lex (Chatbot) Solutions
- Automatic speech recognition (ASR) (speech to text)
- Natural language understanding (NLU)
Amazon Polly Solutions (~25 Languages)
- Text-to-speech marks
Other Amazon AI Solutions