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4 Ways RPA Will Transform Marketing in 2024

Marketing is becoming an increasingly important focal point for businesses. According to the latest survey done by Deloitte, the average investment amount in the marketing departments is the highest it’s ever been.

However, the expansion of the scale of marketing efforts brings with it many challenges, including higher workloads for the staff and the scarcity of time. RPA has the potential to step in and take over some manual and repetitive tasks that are foundational to all marketing efforts.

In this article, we will be going over 4 use cases of RPA in different marketing tasks that can reduce the workload of your staff and give them the privilege of time to focus on more strategically important duties.

1. Automated pricing

One of the important marketing tasks is to ensure that the price of the product that is listed on your website is:

  • Internally accurate (i.e., reflects the actual price the company has agreed upon),
  • And, competitive.

Automated pricing software enables companies to set a price that meets the company’s expectations, and is in line with the financial investment that has gone into developing it.

These applications also use a collection of other AI technologies as well. For instance, they leverage web scrapers to regularly scrape competitors’ websites to see the price they’re charging for a similar product.

Then, the intelligent RPA bot, that is powered by OCR and NLP, will read the different prices and compare them with the company’s. Finally, it can automatically automatically increase/decrease the price within a certain margin.

The benefit of automated pricing algorithms is that it reduces the workload of the marketing employees. The other advantage is that it gathers competitors’ prices with the highest level of accuracy and speed, thus enabling the company to capture profit margins.

2. Monitoring the competitors

Closely related to competitive pricing is the notion of competition monitoring. Marketers should assess how they fare compared to the competitors, not just in terms of price, but also in different factors.

For instance, they would want to know what images the competitors use in their advertisement campaigns to take inspiration from. Or alternatively, to get a sense of what sort of photography and imagery is popular with customers today.

Just as web scrapers can scrape pricing data off of competitors’ websites, they can be programmed to get the color codes of images that are on their websites as well. For instance, the color pink’s code is “FFC0CB.” The web scraper can collect the color codes of advertisements’ images to provide the company with an insight into the colors that are currently resonating with customers.

The benefit of using automated web scraping in marketing is that it is:

  1. Scalable: It can gather data on any number of factors,
  2. Efficient: The robot will work faster than a human in opening up competitors’ websites, going on to the desired endpoint, collecting, and presenting the data.
  3. Accurate: Because the web scrapers work in a rule-based manner, they will be programmed to start scraping at a certain time and collect the information as they appear on the websites.

We have a dedicated article that discusses the use cases of web scraping in marketing in more detail.

3. Data management

Various digital marketing sources (i.e., social media, email marketing, etc.) are providing marketers with more data than ever before. To make the most of these incoming flows of information, marketers should organize and structurize them.

Enter, data management platforms (DMP) that automatically collect and organize these data and allow marketers to capitalize on them. Let’s say the marketers want to improve on a specific aspect of customer experience: the delivery time of a service or a product. DMP software allows them to sort the customers who’ve made orders through Facebook within a specific time frame and the delivery time.

Through RPA, they then can send individualized Facebook messages to customers, asking for their feedback on the matter. The benefit of the integration between DMP and RPA is that it makes outreach, en masse, feasible. The other benefit is that the data that’s coming your way is always structured and is ready to be put into use.

3.1 Social sentiment collection

A very specific use case of data management is capturing social sentiment. They are helpful for marketers to get a sense of how audiences are responding to the product, what they want to see in the future, and where they could improve.

In today’s digital, omnichannel climate, however, there is no single source for the social sentiment. So marketers should keep track of the company’s social media platforms, website, email address, and anywhere else where the customer could leave their comments on.

Manually gathering, wrangling, and presenting the data could be time-consuming and error-prone. Marketing teams can leverage web scraping that uses RPA to collect social sentiment for them. They then can, through API, exchange the social sentiment data with a social media marketing services solution, for instance, to increase their online presence.

4. Bid adjustment

In e-commerce, marketing teams work with digital advertising platforms, such as Google ads or Instagram, to showcase their company’s products and/or services. One factor that plays a crucial role in the success of digital advertising campaigns is the time at which an ad is displayed most frequently. This is referred to as “advertisement bidding.”

Ideally, marketers should leverage data to determine when and how their ads should be displayed the most. Information such as the time of the day, the audience’s age, gender, device, and other points of interest should be driving those PPC (pay-per-click) campaigns.

But going through and analyzing these data, in addition to automatically programming your advertisement bid to adjust in relation to the changing paradigms of the data in real-time, would be a round-the-clock task.

Automated bid adjustment, or automated PPC software, uses API to connect your advertisement bidding and the data sets, coming from your audience, together. From there on, it leverages RPA to automatically adjust the bid according to the data it’s receiving. For instance, if the data coming in from your ads show that your youth audience engages the most with your ads between 5 pm and 7 pm, the software automatically increases bidding in those time slots.

The benefit of this software is that it doesn’t need constant oversight from an employee. Moreover, no marketing opportunities will be missed by overlooking the data. The marketing teams can rest assured that the bots are capitalizing on the data that’s being generated and is coming in.

For more on RPA

To learn more about RPA use cases across different industries, read:

For a comprehensive overview of all RPA use cases and specifications, download our whitepaper:

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And if you believe your business would benefit from implementing an RPA software, we have a data-driven list of vendors prepared.

We will help you choose the best tool for your business:

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Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
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Cem Dilmegani
Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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