Are bots any good? A Brief Overview of How AI is Changing Marketing
Updated: Aug 17, 2020
Image Credits: Swisscognitive.ch (2018)
Although the 4Ps’ are still the fundamentals of marketing, technology is drastically changing the processes and methods which constitute an efficient marketing strategy.
Quantitative techniques and derived inferences have always held a high pedestal in the profession. Consequently, data has been of paramount importance. With the boom in AI, things are changing and they are changing fast. When put under the AI lens, rich consumer data is helping businesses thrive and be more profitable than ever.
“Amazing things will happen when you listen to the consumer,” remarks Jonathan Mildenhall of AirBnB. The consumer in today’s data-driven world speaks through multiple channels and the algorithms are listening. Cookies, preferences, location data, and other such attributes provide all that the AI models need to understand consumer behavior and enable a high degree of personalization.
Super-personalized marketing and advertising which was only a theoretical concept a decade back is now a reality. Deep learning has been an important enabling factor. Optimization and fine-tuning help identify trends and augment professionals like never before. The efficiency with which algorithms make inferences has been increasing due to improvement in hardware and new & innovative data analysis techniques.
NLP through Deep Learning has been the path-breaking development in this field. Researchers are finding new data sources to enable a better understanding of consumer behavior. For example, NLP algorithms are being used to transcribe customer service calls and help in identifying consumer-sentiment or a preference shift towards alternate providers, as measured by the term-frequency in transcriptions. Another interesting application is aggregation and analysis of large-scale review data and isolating common issues which may be then corrected.
Dynamic and real-time consumer insights are a boon to marketers who would earlier be dependent only on lag variables like sales data, or surveys. Text analysis of dedicated twitter threads and product-specific hashtags helps data-driven marketers to understand consumer sentiment and make suitable changes to their strategy.
Companies get thousands of leads per month, how many of these leads result in actual sales, is a question that marketing experts have been researching since the field formally came into existence. Text-Analysis and predictive analytics has helped dis-entangle this complex problem to a certain extent and the prize for the efforts is improved more focused and targeted marketing. Lead scoring using deep learning has helped businesses scale down on teams and enhance efficiency, simultaneously. Models can help determine a prospects' intent and understand patterns buying process along with recently purchased services, to infer if the client is ready to make a purchase. These insights are then used to target marketing efforts to the most “hot” leads and improve sales.
Image Credits: Salesforce.com (2017)
The impact of AI is becoming more evident than ever before. A report by Salesforce found that high performing companies are twice as likely to use AI than underperforming companies (Salesforce Research, 2018). However there are several issues with the use of AI especially marketing, the workforce and professionals are still not technically proficient in the use of AI and data-driven marketing does require a certain level of technical know-how. A simple solution is the abstraction of the computational processes and making this technology more user friendly, at the same ensuring that there is no compromise with efficiency.
We, at Neuralastic, are already providing clients with chatbots and consumer sentiment analysis tools, among other AI solutions, to help their businesses leverage AI and market better.
Head over to www.neuralastic.com to know more.
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