I love marketing. I find it to be a fascinating intersection of creativity and business savvy, and it has been an area where creative people have worked and thrived.
However, as Bob Dylan sang, ‘the times, they are a-changing.' Digital channels now require and provide vast volumes of information. To optimize the efficiency of the marketing efforts, marketing organizations are required to collect a lot of data about the customer behavior and how they interact with the marketing content. Marketers also need more content to be produced and disseminated to an increasing number channels.
It is difficult to create, manage, analyze, and optimize huge amounts of content manually. It can’t be done using creativity alone. Even the best analytics tools and content management systems cannot help an organization to make the right decisions at the speed of digital—at least not manually. Not only does Web and eCommerce personalization require super-fast number crunching to present the right message in real time, but also significant amounts of granular product information are needed to put the product story together.
Not even with an army of super creative marketers would it be possible to do this manually. Customers require relevant results in real time. It also becomes increasingly difficult to manually produce all the content that is needed to put the right message together for your individual customers, at all touchpoints, for all stages in the buying journey. When you add the requisite analysis and optimization it to the mix, you will find that a new breed of solutions is needed.
Enter Artificial Intelligence (AI) and Machine Learning (ML) to save the day.
How can AI and ML help?
AI can be used to make split-second decisions. Combined with ML, AI can learn how to make better decisions over time. AI can also adapt to changes in buying behavior long before any human has had time to put a report together, analyze it, test a new idea, and iterate until it works. Most of us interact with AI- and ML-powered behavioral recommendation engines every time we shop at Amazon and many other retailers’ sites. AI controls product recommendations, provides optimized guided navigation, and adds inspirational suggestions, all based on customers’ behavioral patterns and buying habits.
This development changes the role of the marketer. AI and ML will take care of most of the merchandising—automatically and in real-time. But a behavioral merchandizing engine needs fuel, a lot of it. This fuel comes in the form of large volumes of high-quality, granular product information. In addition, to be efficient, AI also requires the right content to create a relevant, personalized and compelling product story.
How does the marketing department know if they produce the right content in the right volume? The answer is: by getting feedback on how the content performs in all their channels and then crunching the numbers. Creating too much content adds unnecessary cost, and creating the wrong content might be even more expensive as it is detrimental to sales. Thus, optimizing content production to produce just enough of the right content is crucial to be successful.
Again, AI and ML come to the rescue. Not only can these solutions control what content that the marketing department produces, but also it can create it as well. For example, companies like Automated Insights provide AI/ML-based services that can write product descriptions automatically based on product specification and categorization data.
Get ready now!
AI and ML will not replace marketers anytime soon, but they will dramatically change what marketers do and how they work. These technologies are already a critical component of online merchandising, and are spreading to all other areas of marketing. Companies need to invest in solutions that can help them move at the speed of digital, but also introduce new roles and ways of organizing the marketing department because AI and ML are going to play a significant role in marketing going forward.
I recently published a blog post about the benefits of using subscription-based business models called "What bike rental and the future of software have in common." Many industries are going through a shift of revenue streams from traditional one-time sales to products being provided “as a service” and paid via subscriptions. In the software industry this is referred to as Software-as-a-Service or SaaS. However, there is much more to this than just switching from a perpetual license model to a subscription model.
The “false cloud”
Back in 2010, Salesforce.com CEO Marc Benioff warned attendees at the Web 2.0 Summit in San Francisco to beware of what they called ‘false clouds.’ Benioff said “Companies hosting private cloud architectures do not benefit from economies of scale that ‘real’ cloud offers.” Back then, Salesforce.com's 77,000 customers were running on 3,000 servers spread over three global data centers. Theoretically, 77,000 companies of varying sizes would require at least 100,000 servers to independently run their CRM platforms on-premises or in a hosted solution. This translates to an equivalent output at only 3 percent of the infrastructure needed because of economies of scale and more efficient hardware utilization.
Many software companies are touting that they can deliver their software in the SaaS delivery model. However, what they are offering is often not a modern multi-tenant cloud solution where all customers are running the same software using shared resources and receiving software updates continuously without costly upgrade projects. In addition, these are not true SaaS offerings, where business users can configure functionality that would otherwise require expensive and time-consuming development using conventional single tenant software. These ‘false cloud’ solutions are often marketed as a ‘Private SaaS’ or a SIP (Secure Isolation Platform), but, in reality, are often just another way of selling an on-premise software package as a hosted solution.
SaaS is much more than cost savings
The biggest drawback with the false cloud is not that the customers are missing out on the economies of scale by not sharing resources, but that they will not have a speedy deployment, a future-proof and configurable solution, and the business agility that comes with a modern SaaS platform. 75% of enterprise software decisionmakers surveyed by Forrester rated ‘business agility’ as the top benefit of a SaaS platform, while another 72% rated ‘speed of deployment’ as a key benefit. Saving money, getting better uptime, and higher security are, of course, still relevant arguments for SaaS, but being agile and fast is of even greater importance. This is especially true for software that supports the rapidly changing processes in sales and marketing that can really reap the benefits of the SaaS model.
Software-as-a-Service is not just your software running on someone else's server. It is much bigger than that and should be an important factor when you choose your software vendors going forward.
Johan Boström, Co-founder and Evangelist, inRiver