January 3, 2019
Jennifer Flanagan is vice president of marketing at Adtaxi
Artificial intelligence is now playing a greater role in capturing and developing relationships with consumers, and machine learning is one field of AI that has marketing potential to harness.
To truly understand machine learning's potential, deeper insight is required to fully execute its impressive applications.
Building trust and improving experiences with chatbots
Conversational interfaces continue to improve consumer relations, ultimately opening the door for marketers to use chatbots in an array of applications.
An intuitive form of conversational AI, chatbots are used across sales and customer support to provide customers with 24/7 self-service on their own terms.
Not only does this machine learning technology align with the millennial mindset, but it is also hailed by industry research firms as a dominant force amongst decision makers in the purchase process.
Chatbots extract and sift through data quicker than humans can. The result? Better delivery of more personalized consumer experiences. This is especially true for customers desiring self-service.
As such, the need to fulfill a task independently now outweighs the need for another person when technology is the desired choice.
Instead of being instructed to "press three to speak with a representative," chatbots are quick to extract details to direct prospective customers without delay. This allows users to naturally communicate based on personal preference.
Brands can effectively implement chatbots to serve as the first point of contact for engagement. What better way to drive up interactions and generate leads to funnel into the appropriate channels? Moreover, it is a definitive resource with which to operate more efficiently, add value, and build brand loyalty.
Prioritizing personalized care
Top brands are adopting machine learning practices to identify, predict and take action on trends affecting product or service development. With more insight into leveraging machine learning, brands are better equipped to improve upon existing products and services.
Savvy brands use machine learning to launch innovative strategies for better customer experiences. It is the orchestrated efforts to attract, sell and serve that win out when enhancing customer satisfaction.
Another competitive strategy for market leadership hinges on customer intimacy — provide unique methods to personalize service and customize products to meet differing needs.
AI is better at learning what is effective for prospects and consumers.
Improving targeting accuracy
As with any marketing strategy, there is always room for improvement.
An investment in machine learning allows marketers to create targeted advertising effortlessly and effectively. The value of doing so – besides giving marketers valuable data on consumer behavior – is that advertising decisions are carried out in real time.
With an AI-based model in place, the decision on when to display targeted ads is entirely up to you.
There is a heavy reliance on data, but as it increases, so does the challenge to manage data effectively.
Machine learning streamlines the processes of data analysis in advertising.
AI is the go-to process when brands want to target potential customers with precision.
For example, Facebook Business Manager is an important advertising tool. When a target audience is created based on interests, machine learning enhances the strategy. The targeted interests are all generated via machine learning, which is the same algorithm that analyzes data users voluntarily input into their profiles.
Optimizing pricing
For brands struggling to set appropriate prices, machine learning can help.
Since consumers have a wealth of options with which to compare prices, brands must factor in specific competitor criteria to accurately set prices.
Machine learning's algorithms generate data to better detect emerging trends or new demands. Brands benefit most from predictive models to best price products and services.
Next to autonomy and speed, the biggest advantage of using machine learning to optimize prices is globalization.
Imagine manually inputting pricing parameters into multiple channels. Then there is price changing, which can impact product sales in other areas.
Machine learning crunches the numbers more accurately. It even anticipates market trends when brands need to make appropriate decisions to adjust prices.
Lastly, when competitive pricing strategies are paramount to success, it is possible to use machine learning to crawl the Web and social platforms to collect essential information about competitor prices for identical or similar products.
You can also analyze customer feedback, popular deals and pricing history over a period of days or weeks.
An intelligent system like this will constantly learn the digital landscape to give brands more leverage over the competition, which leads to wiser pricing decisions.
Determining incentives and sale offers
Brands can further optimize marketing efforts by defining which sale offers, promotions and incentives to present to prospective customers. This is accomplished through the use of multiple channels, which is yet another machine learning strategy that is transforming marketing.
Specialized sale offers are created through contextual information, offers and incentives. Each is systematically structured using machine learning.
In turn, AI regularly predicts the best possible combinations of marketing elements to generate an upsell, cross-sell or new sale entirely.
AS BRANDS WORK vigorously to ensure that products are exposed to the right consumers, advances in AI continue to emerge to help reach marketing goals.
With proper planning and execution, machine learning has the potential to have an exponential effect on advertising.
Jennifer Flanagan is vice president of marketing at Adtaxi, a Denver, CO-based digital marketing agency. Reach her at jflanagan@adtaxi.com.
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