Technology is constantly evolving. Yet, many companies want to do what businesses still naively believe “if it ain’t broke, don’t fix it.” This is particularly true if the technology is core to their business model and has been successful.
When it comes to search engines, it’s hard to deny Google has been super-successful — and has dominated search engine queries for years.
But that dominance has a new challenge, thanks again to technology changes.
IoT Query Results
Increasingly people are using voice controlled assistants to trigger activity from other smart devices or, more critical for search, asking for information.
This creates Internet of Things (IoT) query results (IoTQR) — search results based on the query phrases consumers say to a smart device.
The quality of queries from IoTQR differs significantly from a search engine results page (SERP), making marketers reconsider how digital media should align to query phrases as well as with online search patterns of keywords.
Ongoing Incremental Change
This is not an entire overhaul of how online search works. In fact, it’s part of an incremental change generated by alternations in search engine algorithms over the years to accommodate mobile search queries.
Consumers have used smartphones to speak their query entries rather than typing a query.
IoTQR also means a query evolution from a singular keyword, like restaurant, to compound consideration, like a geolocation consideration (asking where is the nearest restaurant) or a conditional activity (place an order at 2 pm).
Machine Learning Meets Search
The arrival of smart voice-controlled devices like Amazon Echo and Google Home introduces these IoTQR conditions for machine learning models to consider in the resulting data, taking advantage of consumers experiencing a totally different interface.
Amazon Echo occasionally uses the Bing search engine as a query source. So when Echo does not understand a provided query, it turned to Bing search results.
That changes what words are fed autonomously into search results, from very general keywords to complete phrases that can indicate sentiment about the items being asked about.
Plus, customers are in front of a device instead of a laptop when querying, creating a different context associated with the keywords used.
Welcome Conversational Marketing
The adoption of voice-controlled assistants offers marketers a straightforward entry into conversational marketing, a tactic involving phrases to build a brand and to establish customer mindshare. Those tactics are crucial, given the high demand for IoT devices among consumers.
The IoT movement is a critical emerging tech trend for marketers, allowing them to scale customer engagement opportunities with — wait for it — technology changes.
For example, eMarketer notes marketers view IoT devices as viable platforms for near-term business objectives because more devices are being introduced into the marketplace. How many? IHS predicts 30.7 billion devices by 2020.
Machine Learning Creates More Change
The impact of machine learning is a richer data quality to be mined during prototype modeling.
Machine learning relies on historical data and live data to produce several prototype models. Analysts then select the best model to be deployed on live data.
Those models that account for IoT influence on search can better refine how certain queries align with when and where queries occur and offer more accuracy to suggested next steps when analyzed.
I find the Bing connection to Amazon fascinating, especially when both Microsoft and Amazon have machine learning initiatives.
But before Bing supporters rejoice, they should take stock that Google will not relinquish its search engine throne anytime soon.
Deep learning is influencing the algorithm powering Google search. Google relies on deep neural networks to increase search engine efficiency in finding query-related media.
It deploys pattern recognition systems that perform specific tests, analyzing vast amounts of data and refining results.
And Google announced at Google Next recently that is also using machine learning to improve image recognition in YouTube, effectively the second largest search engine by usage next to its main search engine.
The Fate of Search
The volume of search conducted through IoT devices is still a sliver compared to that of mobile search let alone search overall.
Moreover, search engines can provide associated metadata information, a detail lacking in IoTQR at the moment. So the difference in results will attract users needing different information.
But it is clear that as we begin to incorporate devices into a responsive network for consumer queries, more marketers will leverage machine learning to develop sophisticated customer experiences and solve consumer needs.
At least until the next technological change.
Pierre DeBois is the founder of Zimana, a small business digital analytics consultancy. He reviews data from web analytics and social media dashboard solutions, then provides recommendations and web development action that improves marketing strategy and business profitability.