How RPA Can Improve Your Digital Marketing

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15th January 2023
5 Minute Read

Why RPA is important for Digital Marketing


For many years marketing departments experienced a bottleneck in manually analyzing the flood of data at their disposal. Marketers became experts at sourcing data. However, the human pinch point in analysis often resulted in delayed marketing decisions that couldn’t be afforded.

The solution arrived in the form of RPA, which offered the capacity to analyze bulk data quickly and accurately, and from which timely decisions could be drawn. This improved agility enables marketing departments to be that much more responsive in making their recommendations. RPA can be implemented to interact with a variety of digital systems in the same way a member of staff would manually, but much more quickly and without errors.

As consumers become ever more confident with their digital interaction, their demands grow accordingly. Marketing departments must constantly adapt to these changes to avoid alienation. Data is gathered from many sources and its astute interpretation of this data that is key to understanding and adapting to customer behavior.

Previously, interpreting marketing data manually was a long-winded process, and the results could rarely be described as categoric. Now, with the advent of numerous analytic tools and marketing data, it can be analyzed for each step of the sales process.


RPA use cases in Digital Marketing 


1.    Competitive analysis

Constantly keeping alert to competitor changes is fundamental to understanding the market. It’s a necessary drain on time and resources so marketers can react to the changing prices, behavior, and new campaigns. Gathering intelligence manually is a slow and laborious process, often involving a visit to each website and transferring many product prices for analysis.

RPA in digital marketing is designed for just such an application. The software can be coded to collect the same data automatically and as frequently as required to keep on top of trending products or more general market changes. The interpretation of the collected data is quickly executed, with none of the human error associated with a manual system.

With quick and accurate results marketers can action the conclusions with appropriate price changes and fine-tuning of strategies.


2.    Advertising Bid Adjustments

PPC is another application that can benefit from the speed and reliability of RPA as opposed to a clunky manual system devoid of dynamism. An RPA implementation can assist with scheduling online ads and make these more appropriate according to current events, trends, and even the weather. Individual demographics are similarly part of the overall buying decision including factors such as the time of the day, the buyers’ age profile, their gender, and their chosen device for purchasing.

RPA can also monitor ad performance and adjust the bid price of subsequent ads to optimize spend and return. For example, by analyzing sales customer intent it can be narrowed down to timeframes where bid prices can be adjusted accordingly. Once implemented and tested, such a system can be left to run autonomously with little input from employees. It would be impossible to achieve the same goals manually without the 24/7 presence of someone to oversee the processes.


3.    Social media scheduling and monitoring

RPA is now well-established as an important tool in the scheduling and monitoring of social media. The scheduling of posts is reliant on social media analytics, so messages are posted when they will have the greatest impact. RPA can also be utilized to reply to messages with an automated response.

The data from social media analytics is of growing importance for companies. With such a subjective medium trying to sift through the analysis manually is time-consuming and burdensome, which makes drawing exact conclusions difficult. RPA has the capability to cut through the amount of data and highlight, for example, why a particular post generates such interest or why a particular ad leads to so many sales.

The data gleaned from social media campaigns have a profound effect on the plans of the social media marketing team. From these insights, they can modify or completely change a campaign depending on customer trends or external market forces.


4.    Technical SEO reports

It’s often the case that ensuring company websites are fully SEO optimized is a never-ending cycle of audits, identifying errors, and implementing fixes. As a manual process, the IT department has its work cut out, preparing, and running manual audits and creating a report, which details the audit outcome. With a manual system, the timing of audits can leave long gaps when errors have not been detected. This can place the system under considerable strain until the following audit highlights the problem.

With an RPA SEO-optimized implementation the system can not only carry out the same SEO audit in a fraction of the time but also monitor the system to pick up any errors very soon after they occur. With a broken inbound link, for example, RPA can be programmed to alert IT staff immediately to rectify the situation, whereas, with a manual system, the problem wouldn’t be highlighted until the following audit.

Such an automated system saves considerable man-hours in the IT department, which can be devoted to other more meaningful and value-driven tasks.


5.    Nurturing Leads

It’s a double-edged sword that technology has introduced shopping to our homes, while at the same time teaching buyers to be savvier with their spending money. Prospective customers now need to be nurtured through their buying decision, so each subsequent buying objection is smoothly overcome on the way to the final purchase. In a face-to-face situation, the skills of the salesman should be a fine art in persuading the customer to buy.

However, with online sales, the distance between the customer and the sales team can make positive dialogue impossible. Fortunately, RPA in digital marketing is a powerful tool to encourage customers towards a buying decision. RPA can be implemented in a variety of lead nurturing tasks including:

  1.   using an algorithm to automatically identify high-quality prospects
  2.   qualifying leads according to their likelihood of buying
  3.     retaining leads with subsequent campaigns so customers feel engaged

With many of the routine tasks of the sales process taken over by RPA, staff members have the time to concentrate on dealing personally with more complex customers’ inquiries.


6.    Automated Pricing

When pricing any product on a website companies calculate the costs associated with manufacturing the product and the profit margin they want to realize on its sale. This process can be automated and enhanced with the use of AI technology. The AI is coded to search the Internet for competitors’ websites and retrieve the prices of all comparable products.

At this stage, RPA is combined with OCR and NLP, which reads the competitor price research and compares it to the projected pricing for the company’s products. The RPA can adjust the product price up or down, depending on the competitor’s results.

Automated pricing algorithms such as these can be deployed whenever required, rather than relying on a time-consuming manual process. So, there is considerable time-saving as employees are not required to carry out lengthy pricing research. Once implemented, automated pricing is considerably faster and more accurate than a manual setup.

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