Social Business Intelligence: Reducing Risk, Building Brands & Driving Growth With Social Media

Most get stuck on how to start. It is important to have a clear vision of how AI should be used to drive ecommerce and how to execute. Companies struggle maximizing on data projects by not having the correct expertise on the right project. In the United States, for instance, there were approximately million workers in , but only , data scientists. Most companies are committed to collecting and storing data but lack resources and understanding to identify the good from the bad. Companies are often unsure how to stack rank AI spend against other technology and information spending. Often the CIO and CTO need to align and pool budgets to execute successful projects where data is captured, stored, accessed, and processed.

It is critical to move from a passive state of AI exploration to an active state of piloting projects. Make your retail site more flexible and innovative while also saving time, money, and launching your site faster. These improvements touch both on automation and saving time as well as making more money faster thanks to better decisions and a clearer path to success.

Many companies are taking a Lean Startup approach and rapidly deploying projects to test and learn. Over the last few years, many of the enduring brands have announced their AI projects on their earnings calls. Even in the past few years, companies that have failed to adapt to customer and technology trends are shutting down operations. The opportunities to better serve customers, understand demands, and offer better products or experiences with less guesswork should invigorate all businesses.

However, it is scary to think about all the accelerated benefits derived from the intelligence and capability awarded to first-movers compared to the wait-and-see crowd. The key to getting started is know which use cases to deploy, have a solid strategy, and pilot small projects for incremental ROI. Many of the companies that are successfully implementing AI are using it to enhance their ecommerce engine and capability to generate revenue through sales and marketing.

Five Reasons Marketers Need to Embrace Social Intelligence

Predictive product recommendation engines can correlate all these data sets to make hyper-personalized selections. Amazon is a common example of how to track buying behavior by account to recommend related products. With AI, you can further automate this process by using apps like Beeketing or a variety of other services. With advances in artificial intelligence and machine learning, new deep personalization techniques have entered ecommerce.

Personalization is the ability to use mass-consumer and individual data to customize content and web interfaces to the user. Personalization stands out from traditional marketing allowing one to one conversations with consumers. Good personalization can increase engagement, conversions, and decrease time to transaction.

For example, online retailers can track web behavior across multiple touch points mobile, web, and email. So the next time a user is on your site shopping for a new laptop, you can send a push notification on their phone with a discount code for the new line. Tools and apps like Choice AI , Smile. Dynamic pricing is a strategy based on which retailers change the price of the product based on supply and demand.

While having fluctuating prices are not new happy hours, stock market, airline tickets , the data we can now access unlocks new potential. We can now append customer data, competitive pricing data, and sales transaction data to predict when to discount, what to discount, and dynamically calculate the minimum amount of discount needed to ensure a transaction.

Dynamic pricing algorithms are an emerging field in artificial intelligence and ecommerce leaders may be hesitant to trust the models that inform pricing. Amazon is the current leader in applying dynamic pricing to their products.

Five Reasons Marketers Need to Embrace Social Intelligence

They are seeing massive success. Artificial agents and chatbots are a computer program designed to simulate conversations with human users, especially over the Internet. Artificial agents are being used to interface with customers on ecommerce sites, inform customer service agents how to service inquiries, and even facilitate sales.

It is important to note that bots are not totally self-reliant. They are great tools to help facilitate simple transactions like answer basic questions, set appointments, or triage and provide basic decisions. The eBay shopbot functions as an AI assistant to help users easily find products of interest using natural language. Users can communicate with the bot via text, voice or using pictures taken with their smartphone of images related to a particular product. There is a lack of published evidence to substantiate whether the chatbot has proven to be a significant driver of revenue for the company.

We also employ them for attribute extraction, generating the proper names of browse nodes, filtering product reviews and more. Today we use our own experiences working with customers, past purchase behavior, market analysis, and personas to better understand how our customers may behave in the future. Access to more data, sophisticated neural nets, and processing power is enabling ecommerce leaders to understand their customers and new trends in behavior better than ever.

Our ability to anticipate our customer next move is made possible by the predictive capabilities of AI. Ecommerce inherently produces a lot of data and most companies understand that data is a natural resource. The limited data we can access through any sort of computer analysis can help us see backwards, but what about seeing forward? Can our data tell us how to act moving forward? Leading companies are making data-informed decisions with better intelligence than the competition.

To go from data to action, you need to know what is differentiated about your data, know how to enrich your data with other data sources and have the infrastructure to support it. Reducing Risk, Building Brands and Driving Growth with Social Media , above is a breakdown of Disney consumer persona segmentations that dominate the conversations across the open social universe. These persona groupings were constructed from nearly five millions consumer social conversations over a period of a few weeks.

This level of insight can get significantly more detailed around demographics, geographics, activities, interests, hobbies, etc. This intelligence elevates segmentation to a new level with the ability to personify consumers based on their attitudes, opinions, actions and experiences shared across the open social universe.

Beyond this, a specific customer journey can be constructed with great detail of the various demand moments and decision triggers the consumer goes through leading up to a purchase. With this understanding, marketers can influence these critical moments with promotions, messaging, education, channels, packaging or features.

A Business Intelligence Approach to AI Implementation in Ecommerce

However, many marketers believe that this path-to-purchase mapping is only relevant to larger ticket items, like cars or electronics due to the array of features and options and higher prices. While the customer journey is very relevant towards helping to influence these types of purchases, they are very applicable to low-ticket items.

Unfortunately, this sentiment has been largely one-dimensional, simply telling if the market liked or disliked a brand with no actionable detail. The large issue has been that first generation tools have delivered questionable accuracy when it comes to sentiment.

1. A Streaming World

That can cause some problems in our understanding. For example, above is a mapping of the state of mind of patients have at the various stages of their treatment journeys with a specific cancer condition. Pharmaceutical marketers use this detailed insight to drive their strategy around patient engagement, treatment education and healthcare provider outreach to enhance compliance. Aside from a deeper understanding of your markets, consumers and products, ASI can drive your competitive strategy both on deep inspection and real-time basis.

Your enterprise can know your competitors better than they know themselves by understanding the issues, concerns, successes, flaws, strengths, weaknesses and decisions impacting and driving their business. Companies receiving this intelligence are using it to shape their strategic decision-making and tactical execution on a daily basis. If your competition is not going to listen to what the market is saying about them, then your business should seize that opportunity.

This not only helps your organization gain a strategic upper hand with greater, more widespread market understanding, but also helps to shape your own decisions in beating the competition.

• Orgueil et Préjugés (French Edition).
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• Authentically Catherine--Book 6: Brokenly His.
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• Ecommerce AI: Using Business Intelligence To Drive Implementation?
• Through Cynical Eyes.

Within these discussions across the open social universe there is a wealth of genuine market intelligence on your business, products, services, consumers, markets, industries and competitors that comes from streaming social media. Marketers who are already leveraging this intelligence are gaining a competitive advantage like never before with deeper understanding of their business, markets, consumers and competitors to help set their strategy and execute their tactics.

Mark is CMO of ListenLogic which delivers advanced social intelligence to leading brands to set strategy, mitigate risk and drive innovation. He co-authored the book Social Business Intelligence: Tags customer experience , cxm , digital , digital marketing , mark harrington , social intelligence , social media , web experience , wem.