Episoder
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In this day of global access to products, it is increasingly important to understand your consumers. We understand that mistakes are going to happen, but it is what the company does with the mistakes that is essential. Through social media and reviews, we are given a voice to reach hundreds of potential consumers like us and tell them how great or awful customer service is with the company. If something goes wrong, admit the error, apologize and then make it right. If the issue is posted on social media, acknowledge the complaint, resolve the issue and move on, don’t dodge the problem and definitely don’t blame your consumer for it. This is the first kiss of death for a business of today. As more companies vie for the attention of the consumer, your reputation is one of the most significant assets your organization has and how you manage it will determine your success. I learned a lot from this interaction with my host and know full well that one customer will not make or break a business, but my moving hosts wasn’t about breaking the company, but me exercising my rights as a consumer to spend my money with a company who wants my business.
Join Keith as he discusses digital customer service in our next episode of #thedigitalrevolutionpodcast.
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Are you looking to standout in LinkedIn to gain that competitive edge? Join Keith and Suzanne as they discuss the features and functions of LinkedIn and how they help members be noticed and more importantly, be remembered online. We discuss keywords, profiles, images, Boolean searches and much more!
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Mangler du episoder?
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Have you ever wondered how web developers and merchandisers always seem to get it right when it comes to the target market? How do they always know what will, and more importantly, what won't work from a design and layout perspective. Join Keith as he welcomes guest host Suzanne Kerst to the podcast to talk about a revolutionary concept in marketing - eye tracking.
Suzanne and Keith explore the technology, its uses, and how the hardware works.
Note: The term "gaze" is frequently used in this episode. It is defined as the length of time a viewer spends looking at an object intently.
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Much like digital technology being here to stay, there is another influence that has come and is transforming businesses around the world. The role of artificial intelligence has impacted organizations looking for cost reductions in operations, client satisfaction and developing new revenue opportunities is some amazing ways. In essence AI draws on data collected at every level of business and helps computer systems and disciplines in a company learn from behaviour and predictive analytics. Basically, AI takes the guess work out of the tough business decisions and creates opportunities never before imagined. It is not only very high tech, but it is super cool!
Let’s look at one area for this episode in particular – AI in marketing. When I was cutting my proverbial teeth in the profession, I couldn’t tell you how many decisions were made by gut instinct, or at least, that was what we called it. We would look at a campaign or a product and make decisions accordingly on where to advertise, when, and what the campaign would ultimately look like. When it was successful, really successful, we would be asked how we did it, but our answers were typically gut instinct. Was it really instinct? No, I don’t think so. You see our decisions were drawn from information that we already knew about our customers. We knew what product sold best, we knew what the customers preferred, heck we even knew at what price they would buy the product. But there was no formalized way of demonstrating the insight so we wrote it off as gut instinct. But when things went bad, there was no room to bask in the instinct and we needed to come up with data or information at least that would support our decisions.
Fast forward twenty years and the world really looks different. Marketers all around the world are now looking at AI to make the decisions for us using automated technology. These decisions are based on massive data warehouses, laser focussed analysis, and other predictive information such as economic trends and our target market to maximize our marketing dollars. The amazing thing about using AI is not only its precision, but how fast it analyzes our customer profiles, understanding the best way to communicate with them, and what to say to evoke the buyer decision without the intervention of any personnel. So now we start to look at AI to augment marketing teams basically using it for tactical tasks that require less human nuance. In fact, this is happening more and more in media buying, decision making, generating specific content, and in creating personalization in real-time.
Other components in AI for the marketing department include using computer algorithms drawing from machine learning and this improves automatically through the experience of the system. Really the system draws heavily on machine learning so that it can analyze anything new in the way of data from historical perspectives so that it can inform decisions based on what did or didn’t work to help determine what will work going forward. This has been boosted by digital media and of course big data which helps us understand how our campaigns or ideas are performing across many different channels. The down side is that with so much information sitting in warehouses, we struggle to determine what is valid or what isn’t worth gathering anymore. It is a really fine line. So rather than lose sight of the bottom line, we look to the AI model to help us manage the huge warehouses and to analyze it into usable information. These systems give use usable intelligence of our target market and let’s us use it to make data-driven decisions. Frameworks like Bayesian Learning and Forgetting can help put together a deeper understanding of how our clients will respond to our marketing efforts.
I know what you are thinking, what the heck is this Bayesian thing you are talking about. Let me quickly explain. This is a statistical model that draws on Bayes theorem of predicting the probable event or to test whether a hypothesis is correct. There are a lot of different industries that draw on this statistical model on a granular level. Basically, it takes the known insights, and any new information to align it. Basically the model helps to determine what will work and what won’t. But it does need a constant influx of data to work, and as it collects information, it draws on what it already knows which will help to reinforce previous insights that are helpful and throwing away what isn’t helpful.
But I am sure that you didn’t think you would be listening to a statistics lecture so I think I will drop it there.
So AI helps us make important decisions, but the challenge is that it takes time to get to where you can rely on it. The other problem with the time needed to get up to speed, is that the data must be awesome or it could threaten the results or destroying the reputation AI. But let’s look at something more ground level shall we? Because the technology is relatively new, there is little in the way of best practices in deployment because getting buy-in immediately is difficult. Sure, we can justify the ROI or its efficiency through results, but what about the less measurable data such as how the customer was treated or the impact of decisions on the brand reputation. This is not so easy.
The real benefits of using AI in Marketing are enjoying an increased campaign return on the investment because of the ability to make informed decision on how to best spend the budget or how to more effective engage the consumer. This means that the level of personal interaction is improved which, of course means your customer experience will naturally improve. Another huge advantage is our ability to more accurately measure the results because of the granular analysis that is now available.
There are so many uses of AI in marketing. In fact, I have seen companies use AI in communication decisions like the tone of the message, the frequency and exactly how a customer interacts with the intended message. Another area that we are seeing more and more is using chatbots to engage consumers in seamless conversation. Unlike the human element, these chatbots are capable of provide the exact information accurately every time, and the nice thing is, they never tire so it doesn’t matter what time of day the consumer reaches out, they will enjoy the same experience. Finally I have also seen companies use AI to help them make programmatic media acquisitions. Relying on the technology means that the marketer can look to AI to bid on advertising drawing from information in the areas of consumer interest, their geography, and even where they are in the buying cycle.
There are so many opportunities we have yet to explore with AI and trust me when I say this, the best is truly yet to come.
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Not to sound like your grandparents talking about a foreign world so far in the past that you can’t possibly fathom, but ten years s ago the world was a different place. I know what you are thinking, heck one year ago the world was a different place, but what I am talking about is commerce and how we advertised. You see, companies used to rely heavily on advertising in newspapers, magazines and above the line advertising to. Help with sales and brand awareness and publishers, well they relied on companies to buy the advertising.
With the influx of technology today, business has had to rethink how it advertises and the publishers, those who are still around, have had to develop a new model for advertising sales. In essence the Internet grew up following the models of print closely, so naturally, the immediate attention went to display advertising as it was not only what the business world knew, but more that consumers were comfortable with the model. There were several. Models to choose from back then, companies could advertise on news services, directorial and websites. The problem with relying on the way business used to operate was that these companies would sell their ad space with sales reps and depending on how well the rep did, many spaces ran the risk of not selling out their inventory of ad spots. In short, the model was really, really inefficient. Because of the need, there was an opportunity identified for services to sell the left over advertising, much like the travel model of selloffvacations.com and ad networks were born.
To be truly successful, the ad networks needed a great deal of sophistication so they invested heavily into their infrastructure to build platforms that would connect buyers and sellers of advertising space. While logically, you would expect that these services would include the traditional media outlets like tv, radio and print, but the market was so hot and the practice at the time so niche, they focussed solely on online sales. It was a huge gamble, but one that really paid off.
As mentioned, the model basically bought the remnant or non-premium advertising space off of the online publishers for pennies on the dollar and sold the spots off for a fraction of the original price. Everyone won. But as with everything, the model grew up and services are thinking more strategically by buying blocks of premium advertising space and in turn selling the premium impressions to advertisers. It means that the advertisers are assured that they are securing premium advertising spots and though there is little savings, these spots mean greater exposure through the frequency or location of the advertisements.
The ad networks shouldn’t be confused with ad servers however which are servers used by advertising networks to manage their day-to-day operations. There are two different types of advertising servers out there. First party servers which are owned and operated by the publishers and third-party which are owned by the advertisers. Both types of servers have their pros and cons for example the first-party servers can be extremely expensive to maintain versus the third-party counterparts though the third-party servers present a greater risk due to connectivity issues and security concerns.
The first-party servers allow the networks to manage their inventory cleanly by offering a conduit for selling off remnant or non premium spots to the ad networks and the supply-side platforms, otherwise known as SSPs and the third party servers are built for storage of the advertisements and provide essential data such as impressions and clicks on the ads.
So to recap a little, an ad network is an aggregator of supply of advertisements sold off by the publishers at reduced pricing. The advertiser then sets up their campaigns through the network and the servers through their management tools or using special technology to verify the data of impressions and clicks. It’s the responsibility of the advertiser to establish the parameters of their campaign such as demographics, psychographics, geography and the target market in general. It is the network responsibility to ensure these ads are inserted appropriately. The nice thing for the ad network is that they can sell the same spot, totally unlike print media, by rotating the ads and managing the impressions based on budget, bidding and other factors like day parting parameters.
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There is so much confusion with respect to the difference between organic and paid search strategies. Understanding the terms and when to employ the different strategies will help companies be seen online and more importantly, be remember online.
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One of the most difficult tasks in today's business worl is understanding how to set goals for projects. Afterall, what doesn't get measured, doesn't get managed. Keith explains what a smart goal is and how it is applied in every day language that will help the marketing professional who has either never used SMART goals before or is an expert looking for a refresher.
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Come along with Keith as he explains the difference between digital transformation and digitalization. The world is changing and knowing the difference will help increase profits and customer retention.
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In this episode, Keith discusses what microtargeting is and how it impacts the user. The examples used include the 2016 US federal election. Join Keith in delving deep into the area of microtargeting of socilal media users.
