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How AI Supports Marketing Professionals

When AI is mentioned, people think first about how AI will make jobs on the shop floor or similar jobs irrelevant. For Business Analytics professionals, AI will not necessarily make their job irrelevant but easier and more efficient.

A recent advancement in technology is Artificial Intelligence (AI). A study conducted by Accenture Research states that by 2035, labor productivity will have risen by 40% and corporate profitability by 38% due to AI.[1]

When AI is mentioned, people think first about how AI will make jobs on the shop floor or similar jobs irrelevant.[2] For Business Analytics professionals, AI will not necessarily make their job irrelevant but easier and more efficient.

 

 

AI in Search Engine Marketing

For instance, in marketing, there is a huge potential for AI to support professionals.

A regular analytical job in marketing is to evaluate how to optimize marketing processes. One process is Search Engine Marketing (SEM). SEM specialists regularly need to change keywords and bid on them in order to get high ranks in search engine results. Headlines and landing pages also need to be adjusted to get more conversions. After the first round of adjustments, SEM specialists need to evaluate if the performance metrics improved and based on the results adjust keywords, headlines and landing pages again. Changing keywords and headlines repeatedly is time-consuming and error-prone, which makes the performance analysis doubtable.

Instead of changing everything by hand, AI-based systems can support the analysts. An example of how this is done is the automated bidding on keywords in Google AdWords. The SEM specialist sets the maximum price per click. Based on that information, the AI is choosing a bid per keyword, assuring the best return on investment. For this purpose, the AI must analyze the quality of the advertisement and predict the bidding behavior of competitors.

 

 

AI in A/B Testing

Besides the bidding process, AI can be used to simplify A/B testing of digital advertisements. On Facebook, for example, it is possible to create a campaign with several ads. A way of A/B testing would be to test which image of an ad performs better. However, it is necessary to create several ads with the same text but different images.

Similar to the bidding case, AI can run various scenarios and even adjust the audience. Testing different audiences is currently only possible by creating another campaign on Facebook. Through analyzing past data, the system can predict which combination of predefined text modules, images, and audience combinations are most likely to perform best and start testing those variations. Thereby, a company would reduce money spent during testing and leave analysts more time to interpret the performance results rather than implementing the test. Nowadays tools like “Adext” are starting to offer such an AI-based technology.

 

 

AI on Websites

Furthermore, AI makes it possible to spread dynamic content on a website based on the users buying phase by analyzing user behavior. For example, a potential customer (prospect) is downloading and consuming several pieces of content which is describing a feature of an offered product. The next time this prospect is visiting the website, the landing page will show content containing that specific feature. Thereby, the user will quickly find the information needed and will less likely leave the page and look for other resources. However, if the prospect did not act on the content shown, the AI algorithm will realize that and change its behavior for the next time. This again saves the analyst valuable time which can be used to analyze which content performs best and act upon those insights.

 

 

AI in Lead Scoring

In addition, AI can support businesses in lead scoring. In the lead scoring process, potential customers who showed interest in a company’s product are assigned a score based on their likelihood to buy. For example, the more a prospect visits a business’s website and consumes content, the higher the likelihood for them to buy. An AI-based service can evaluate in real time the prospects activities and calculate an accurate score based on past data. Tools like Salesforce’s “Pardot” for instance, are supporting sales teams during that process, by assigning scores to prospects. Once again, the time-consuming work of gathering data about a prospect’s behavior is taken care of by the AI algorithm. Hence, the analyst has more time evaluating which content performs best.

 

 

Despite the fact, that AI is not widespread, studies show that the usage will increase over the next years.[3] As shown, these developments will not make the job of a data analyst irrelevant but increase profitability and ease day to day business of professionals in all departments. In conclusion, data analysts will still need to verify data and insights given by an AI-based service. However, the role of data analysts might change and move from mainly analyzing raw data to more strategizing tasks.

This essay was created as part for my application for a merit-based scholarship. The essay question is stated bellow. For publication, I added headlines in between the paragraphs.

Question: “Describe a recent advancement or development in the field of business analytics that has influenced your decision to pursue the [Master of Science in Business Analytics] degree at the University of Utah. Describe how this development would influence or improve operations or decision making in a business setting.”

[1] See Purdy/Daugherty (2017), p. 3, 6.

[2] See Manyika (2017), p. 5.

[3] See Salesforce Research, p. 19.

Header Image by Franki Chamaki on Unsplash

 

Manyika, James. What’s now and next in analytics, AI, and automation. San Francisco: McKinsey Global Institute, 2017. Accessed February 24, 2019. https://www.mckinsey.com/~/media/mckinsey/featured%20insights/digital%20disruption/whats%20now%20and%20next%20in%20analytics%20automation/final%20pdf/mgi-briefing-note-automation-final.ashx.

 

Purdy, Mark; Daugherty, Paul. How AI Boosts Industry Profits and Innovation. Dublin: Accenture, 2017. Accessed February 24, 2019. https://www.accenture.com/t20170620T055506__w__/us-en/_acnmedia/Accenture/next-gen-5/insight-ai-industry-growth/pdf/Accenture-AI-Industry-Growth-Full-Report.pdf?la=en.

Salesforce Research. State of Marketing. San Francisco: Salesforce.com, 2018. Accessed February 24, 2019. https://c1.sfdcstatic.com/content/dam/web/en_us/www/assets/pdf/datasheets/salesforce-research-fifth-edition-state-of-marketing.pdf.

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Jonas Vitt

About Jonas Vitt

Born and raised in Karlsruhe, in the south-west of Germany, Jonas is currently pursuing a Master of Science in Business Analytics at the David Eccles School of Business at the University of Utah in Salt Lake City, UT. He finished his Bachelor’s degree in Business Administration with a focus on marketing at Pforzheim University in July 2019.

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