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July 23, 2020
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Using the Right Content Mix to Elevate Yourself into A Thought Leader

By Bob Peret | Time to Read: 00:08:00
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Using the Right Content Mix to Elevate Yourself into A Thought Leader
Marketing

It all seems very apt starting this piece on the very words of Sabine Hauert, the co-founder of Robohub.

Anytime the world of robots or artificial intelligence is discussed, people shy away from the topics due to the stigma on how many jobs these robots will wipe off the table. The disruption is coming – whether singular players back it or not – but those who do not would be the ones to pay the most price for ignoring at an early stage.

So, back, to Sabine:

“Robots are not going to replace humans. They are going to make their jobs much more humane. Difficult, demanding, demeaning, dull – these are the jobs that the robots will be taking”

If that is true (spoiler: it is), marketers should not be left behind on the train that takes everyone else to AI adoption. But then again, why bother?

The Argument for AI in Marketing

The IDC predicts that the world will see its data stash skyrocket by 61% (to 175 zettabytes) by the year 2025. Naturally, the data growth between now and then is met with more spending in the AI space. That saw a 44% year-on-year spike in the money flowing to AI systems between 2018 and 2019 where the spending figures now stand at around $35.8 billion.

Somewhere in all that, 61% of marketers claim the importance of AI to their all-time strategy. The companies that have not gotten in on this trend yet are scrambling to make something happen for themselves.

That leaves us to wonder what the fuss is all about. Looking at the top companies that have implemented AI right in their marketing, some benefits just shine through without much effort.

1 Cost Savings

Companies are in the business of spending less than they make. That is the only way to turn a profit – and every department must be committed to the same goal. For Netflix, at least, artificial intelligence has shown that it can bring massive cost savings benefits to the table.

Back in 2017, Netflix started experimenting with a machine-learning algorithm to suggest movies to viewers based on their preferences, profile, and what they have viewed. This move saved the company a massive $1 billion all around. These cost savings were mostly made up of:

  • Reduced customer churn – thus, the company does not always shave to spend to acquire new customers
  • Improved brand loyalty – the company was able to reduce the rate of consumer switching to rivals
  • Better customer satisfaction – when viewers can have the next best thing waiting for them before finishing the show/ movie that they are on, we don’t see how they will not be satisfied.
    • We can only imagine that the company has saved more money over the past three years because of this algorithm. It is also little wonder why Netflix is known to have one of the best AI algorithms around today since they got in early – and worked fast too.

      2 Consumer Behavior Mapping

      Four years ago, Salesforce released a report offering insight into the model of the future consumer.

      The report predicted that consumers will need brands to know what they (the consumer) want before the first interaction at all. Thus, even if a brand were carrying a lot of products, they should know which one will fit into the profile of the target consumer.

      The numbers in that report were pegged at 57% of consumers – and set for 2020. The year 2020 is here and we can all agree that there is a great deal of truth to that prediction today.

      AI comes in here to:

      • Explore large bodies of consumer data
      • Map out trends that the human operator would have missed
      • Create more complex consumer personas based on even the tiniest data detail
      • Curate a more complete profile for improved targeting and
      • Enhance the brand product manufacturing process to fit the need of the market.

      Without that, the Salesforce report confirms that erring brands will lose their buyers to another brand.

      3 Productivity Boost

      Human resources in marketing are most times wasted on repetitive, routine tasks that could have been better handled by AI anyways. There are two major downsides to such repetitive work:

      • The human resource gets bored with their work and stops innovating. This WILL hurt the marketing (and overall, the business) in the long run
      • Lack of challenge in those roles means the human resource is being wasted away. Again, that is a case of lost cost for the business.
      • This follows the words of Sabine from the opening statement. However, he did skirt the truth a little bit. We can confirm that AI will truly make people lose jobs.

        According to a Gartner study, though, AI will create 2.3 million positions for every 1.8 million that it eliminates. We guess that these new positions will be those that allow for better, fulfilling use of human resources.

        Well, it is not much of a guess:

        • 82% of business execs already leverage AI to carry out their paperwork. About 79% depend on AI for scheduling while a smaller 78% task AI with their timesheets (Source)
        • 31% of these execs believe that AI personal assistants will make a big impact on their business. The same sentiment is shared by 29% of execs who already use AI for data analytics.
        • 61% of companies leveraging AI do not want to stop since they are using the technology to innovate better and faster. (Source)
        • Gartner believes AI in marketing will free up almost a third of data analysts. Thus, their skills can be better focused on more areas of growth while analysis is on lockdown.
        • Those numbers more than speak for themselves. Just imagine what AI will do to the productivity in a marketing department where there are a lot of things to automate today.

          4 Faster Data Analytics

          At the height of the coronavirus pandemic back in January, China swung into action to build a massive hospital facility in less than 2 weeks. The zeal and determination behind this project were seen and applauded all around the world, but we all almost missed the data.

          Truth is that the hospital would not have been possible – neither would it have been as effective – if AI was not involved.

          Taking over satellite imagery, Chinese authorities were able to track where the cases in Wuhan were being reported. This distribution was used to plot the best, central stage of care for everyone in the region that might either be infected or at risk of infection.

          The data from satellite imagery also showed the best location for the hospital of such a magnitude to be constructed. What would have taken human foot soldiers some time to determine was done in just a matter of hours or days by AI.

          Apply that to marketing and the possibilities are almost endless. Consumer behavior that used to seem erratic will suddenly have a clear pattern. Market patterns not favoring certain brands and their clients begin to emerge. Trends for the future, not forgetting disruptions, are quickly identified.

          All of that arms a solid marketing team with all the tools they need to launch a full-out assault on the competition and take most of the market share.

          5 Improve Complex Decision Making

          It is one thing to have data analyzed (like in the point above), and it is another thing to interpret the data right for marketing needs. Data analysts are great at telling you what numbers they can extract from a large body of data but not all of them will tell you what that means.

          The onus lies on the CMO and other C-suite executives to determine which data to pursue and which ones to leave. Even if all data reports are to be pursued, which comes first before the other? After all, time is of the essence in these cases too.

          The answer lies with the top-performing companies that have installed AI into their operations. These companies claim that they are more likely to use the tech for marketing than their peers. Adobe, who chaired that study, stresses that these are the reports from the cream of the bunch. That should speak for itself.

          6 Improved Content Visibility

          The best content will not do well if it is not seen by those it is meant for.

          There was a time when you just had to stuff your content with a bunch of keywords and you would be guaranteed some time on search engine rankings. Even those search engine companies now use AI to make sure that will not happen.

          That does not put you at a loss. It only sets you up to upstage the competition with some impressive insights.

          Pulling insights from in-house historical data, AI can help determine which days of the week are best for putting content out. This can even be refined down to the very time that the content should be sent out. Besides, AI can be deployed to the different content marketing channels to fish out which works best for your target audience.

          Of course, you can just ask the audience for these things. There is always the chance of getting skewed data, though, further complicating the decision-making process. When the data is sourced automatically when consumers are at their natural states, more weight can be thrown behind such results.

          7 Better Sales Potential

          Let’s take a moment to look at these numbers:

          • Gartner reports that more than 30% of companies globally will deploy AI in at least, one sales process.
          • Statista buttresses this point with a tall 61% of companies already using AI pursuing a sales forecasting use case as of three years ago. (Source)
          • 45% of end-users already prefer to communicate with a chatbot rather than actual human representative; and
          • Adobe can confirm that 75% of companies using AI in sales have recorded at least, a 10% jump in their sales. (Source)

          Okay. Let’s lay off the numbers for a minute now. What do these even mean – and how will they come to be?

          In the example of Netflix shared above, AI is not just saving the company a massive $1 billion every year. It is also reported that 75% of user-viewed content comes from recommendations. This boosts the sales of the company since they can somehow identify what their consumers need before they even know it.

          Sophisticated marketers are no longer challenged by the prospect of closing the first sale. The challenge has now moved to identifying what consumers can be effectively offered upsells and cross-sells on their base packages.

          Get this right and you have a premium customer for life. Miss a step and… we don’t have to tell you how that goes.

          AI takes all the actionable consumer behavioral data at its disposal to determine which clients/ consumers are ready to go for more. This technology helps to identify those clients that still have some needs to be filled besides the basic one – and they can get the upsell.

          Some consumers also come in via one funnel, totally unaware of another. Migrating such leads between funnels can be a tricky task. Not when you have AI holding your hand every step of the way, though. Instead, you have a better understanding of who your consumers are, and what more value you can provide to them.

          Limitations of AI to Marketers

          It’s not all rosy in AI Land either.

          For all the benefits that AI brings to the table, there are valid concerns to be addressed for a sustainable and scalable model in widespread operations.

          It starts with the cost of acquiring new technology. Major players in the field are not only getting AI tech, but they are also scooping up the experts that can work on them alongside. This has created a double surge in the demand for AI talent in the past two years alone.

          Marketers will also need to convince the top executives, and fast, lest they lose out on all of the best talents. As we speak, tech and financial-based firms are soaking up a massive 60% of all the available AI talent.

          Likewise, the AI model is dependent on the training that it has received. These systems can indeed learn on their own, but they will only learn based on the environment that they have been developed in.

          Thus, they can never expand beyond the scope of what they were initially trained with, no matter how explosive the expansion. This underlines why these systems need to be properly trained lest they start leaving too much money on the table.

          That leads us to the argument on creativity – or lack thereof – for these systems.

          Humans can always improvise, but AI does not have the depth to make that happen. At least, not yet. Marketing is standard, but there are a series of flexibilities and tweaks to fit the exact systems you have in place.

          When your AI cannot adapt to such changes in real-time, this latency could wipe out your market margins and gains in a smaller timeframe.

          Foraying into AI

          Marketers need to identify their use cases for this technology before they set out to acquire, not after. The company goals, trickling into marketing goals, must be tied to the functionality of the AI system.

          A cost-benefit analysis should also be made to understand if the decision to implement in the desired sectors will yield a substantial ROI in due time. Otherwise, the entire strategy is best re-thought.

          Otherwise, AI spending can fast become a white elephant project that no one wants to touch later. Worse off, the time wasted on chasing AI wrongly could have allowed the competition enough time to set up shop.

Do any of these trends jump out? Get in touch with a thought leadership expert to find out more

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