Gain Actionable Insight into what Customers are Saying About Your Business
Do you know what customers are really saying about your business? In the past, connecting with your customers was generally straightforward because it was on a one-to-one basis. In a more digital world, what used to be more intuitive to ascertain — the wants, needs, and desires — is now not quite so simple. So, how do marketers determine true customer sentiment at scale?
The First Step In Your AI Journey
As search engines such as Google release new algorithm updates, search engine optimizers (SEOs) are constantly having to scramble to ensure their content ranks for relevant consumer search queries. Search engines are deploying AI to better understand the intent behind a user’s search query, resulting in search results that are more relevant to the consumer and ultimately will improve overall user experience.
However, marketers have no visibility into what companies such as Google and Facebook are learning and continue to restrict what data they can and can’t see. Further, announcements in late 2020 from Apple and Google stated that third-party cookie tracking will be turned off, only further complicating a marketer’s dilemma.
Data privacy and transparency continues to grow in significance. Marketers must leverage their first-party data, the data collected from customers and leads on your website, in order to continue to effectively compete in this arena.
NLU: A Factor Marketers Need to Account For
Marketers must turn to AI and machine learning technology for the solution. IBM Watson can help marketers understand their first-party data, and therefore gain a better understanding of what their target customer is searching for. Watson’s natural language understanding (NLU) can analyze millions of unstructured data records, both in written or speech format, and break down this information so that it makes sense.
Website leads like phone calls, chats, emails or texts are all examples of unstructured, first-party data that can be fed into Watson. Watson Natural Language Understanding can be used for further insights such as with the following:
- Keywords to determine the most important words in content, identify general concepts in content, and understand concepts
- Categories to categorize content into hierarchies
- Entities to detect important people, places, geopolitical terms, etc.
- Sentiment to discover whether your content conveys positive or negative sentiment
This parsed information can be utilized by marketers to aid in the development of content that is more relevant to their consumers, which can then be implemented across every aspect of their digital marketing.
Adding NLC and NLU Simply: SherloQ™ Use Case
SherloQ™ with IBM Watson is an AI-Core for websites and ad campaigns. SherloQ™ recognized that the website is the core of all marketing efforts because it captures all first-party lead data, both organic and paid. SherloQ™ utilizes Watson’s NLU to analyze this data across some of the most competitive verticals: automotive, publishing, and legal, to name a few. SherloQ™ creates a “lead scoring model” based on the user’s own patented bio feedback. This modifies the machine learning models by augmenting or weighting the data used by advertising platforms. The AutoAI feature within the IBM Watson Studio application automates the evaluation and selection of the best performing models with one-click deployment from the IBM Watson Machine Learning solution.
At a high level, information parsed from Watson’s NLU is delivered to SherloQ’s™ customers in a dashboard. This allows marketers to glean insights from the language consumers are using when talking about a specific business — insights that can be actively applied to website copy, landing pages, editorial content and ad copy.
Let’s take a look at SherloQ™ in action in an agency setting.
Using SherloQ™, the marketing team at Law.com analyzed over 1,000 organic leads to mine first-party keyword data for a personal injury law firm client located in a top 10 market in the U.S. The agency’s goal was to identify opportunities to improve and optimize site copy on the client’s primary practice area pages based on insights gleaned from IBM Watson.
When reviewing the lead data aggregated by SherloQ™, the agency’s marketing team was surprised to uncover the language consumers were using that they simply weren’t able to tap into — until now.
Once the keywords were scraped from SherloQ™, the marketing team further organized this information by specific practice area and gained even more insight. Regardless of the practice area, almost all of the language consumers were using revolved around a common theme: the person’s specific injury and/or treatment.
For example, some of the keywords found in leads categorized under “car accident” by IBM Watson were “sharp back pain,” “physical therapy,” “surgery,” “shoulder pain,” “pain medication,” and “second opinion.” This resulted in the marketing team developing new copy that discussed specific car accident injuries and common forms of treatment. This same process was followed to make updates to the other pages, as well.
This is just one benefit of marketing teams having SherloQ™ at their disposal. SherloQ™ utilizes Watson’s NLU to analyze first-party lead data, allowing marketing teams to uncover the specific language consumers are using to describe a business so that they can then apply those insights to develop high-quality, relevant content that speaks to the user in their own words.
Learn more about IBM Watson Natural Language Understanding
Explore some of the latest NLP research at IBM or take a look at some of IBM’s product offerings, like Watson Natural Language Understanding. Its text analytics service offers insight into categories, concepts, entities, keywords, relationships, sentiment, and syntax from your textual data to help you respond to user needs quickly and efficiently. Help your business get on the right track to analyze and infuse your data at scale for AI
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