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Marketing Strategy Development: How to face AI challenges

Unfortunately, many people fail to properly understand the marketing strategy development potential of AI. This lack of understanding affects the rate at which it is taken up. It also has another unusual effect: 40% of business owners already believed they were using AI. This failure to understand the possibilities of AI therefore lead to the stagnation of development in this particular field. It is, however, up to people already in the field to spread the word about their successes with AI and chatbot technology.

AI offers the opportunity to liberate businesses from the intricacies of digital marketing strategy development. Through learning from past behaviour, they can accurately predict how best to proceed. They can make this prediction at speeds not previously seen. Algorithms are bringing dependability and accuracy to marketing strategy development. However, there are further challenges to be overcome before marketers can just relax.

The various AI challenges to marketing strategy

Some of the most regular challenges facing business owners who adopt AI are:

1- Failure to maintain customer-focus

  • 63% of marketers felt that their integration with AI caused them to lose focus of the importance of the customer. AI forced them to look far more closely at data, metrics and analytical strategy than on the customers themselves, resulting in a loss of community and emphasis on the customer journey.
  • It’s important to maintain a close eye on your brand, and its people. If you are outsourcing the adoption of AI, ensure the people involved have an intimate relationship with your brand / industry. Data isn’t everything, people are at the heart of your success and your rationale for continuing.

2- Lack of Direction

  • Often, what’s missing is a decent strategy to bring everybody up to speed in terms of abilities, positions and policies. A learn-on-the-job approach is often expected from managers in these situations, resulting in a confused and unfocused workforce.
  • Ensure that policies and training are in place and well understood before the roll-out of AI. As well as this, make sure that people are aware of their place within the organisational structure. Direct engagement is still at the heart of AI, led by people. People control the outcomes of what AI is set to do, so ensure that people are at the forefront of your marketing strategy development.

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3- Low-Quality Data

  • Many involved in the adoption of AI complain about the poor quality of data and analytics, leaving them without leverage to make changes and improvements or make decisions.
  • Not all metrics are created equal: some are designed to be used and understood at the level of gross revenue, while others are designed for the analysis of broader concepts such as brand direction, value, customer retention and recurrence. Make sure that your metrics focus on all aspects of your business, and that they are clearly defined as to their parameters and their outcomes.

4- Distractions

  • You can be easily bogged down in the marketing buzzwords of the day. The business and competition of the marketing world can lead people to take on AI too heavily and rapidly.
  • Introduce AI to a specific area slowly and methodically and only after fully understanding its purpose and effects. Develop areas and have them ready for AI otherwise it will fail at the first hurdle. The same goes for your workforce, AI is nothing without a strong, capable team to implement it in each specific area.

5- It costs more than you think

  • Whenever we embark on new projects it’s easy to fall into funding traps, just adding a little more money here and there to solve problems as they arise. Costs quickly double.
  • Take another look at the budget, compare it against what others spent, and account for all the other steps mentioned in this article. Managing unknowns is an enormous part of marketing, make sure your roll-out of AI is included.

Marketing strategy development futuristic
Get ready for a rough but rewarding ride

These are some of the potential pitfalls you might face in AI. By following some of these guidelines you will be well prepared. Importantly, don’t forget about these key factors: Education, industry specific applications, prepared use cases, be unique, partner up with relevant agencies.

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