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Understand township consumers and what they value well enough to really add value to their lives through marketing efforts
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Netflix’s recent announcement of their international expansion in 2016 is not unexpected, but still somewhat breathtaking in its scope. While it may seem natural to those in the United States, where Netflix holds a dominant position in the Subscription Video on Demand (SVOD) space and in other early markets where it is a well-known brand, but this latest overseas growth is not as much “a sure thing” elsewhere.
Certainly Netflix will enter these new markets with a well-known brand name, which may be less connected to its actual content than to the fact that US-originating digital brands often have a leg-up on local brands. Netflix will generally appeal to affluent, Western-oriented consumers outside of the North American and Western European markets.
But Netflix will have a number of concerns when entering these other developing markets that make up much of the dozens being added. These include:
That being said, Netflix has consistently outperformed expectations of industry experts and those in the financial markets. Its daring moves in the past have mostly panned out. And, aside from content, it has an understanding of its consumers – through the use of its own collected big data – with which few of its potential competitors can hope to compare.
As for its competitors, frenemies, and partners – some being all three – the growth of Netflix raises questions that only third-party accounting of Netflix can answer. This way their competition or partnership with Netflix is on a more level playing field.
What do you think about Netflix’s expansion? Do you see other challenges? I would like to hear your opinion as well.
For more information, please contact me at firstname.lastname@example.org.
Segmentations can be an incredibly powerful tool for businesses, providing strong platforms for innovation and a targeted approach to customer relations. However the strength of segmentation hinges on the level of similarity between the individuals in each segment. The greater the similarity the more comprehensively the group represents its’ individuals and as such the more accurately it predicts their behavior.
The more angles that we can describe the individual from the more points of similarity we can draw between them. Each individual angle is only part of the picture of the individual, like the sun shining on the moon.
The majority of segmentations are based around one angle; the articulated views of an individual, what they say they do, what they think, what they want etc…
However there are a couple of inherent problems with this, firstly articulation statements can be very hard to write, and must be carefully thought out in order to ensure they resonate and are interpreted in the same way for consumers, particularly across international borders. Take the statement “It is important to me to eat healthily”; there are a number of areas that this statement can be open to interpretation by the respondent; what is the definition of healthy? How important does it have to be?
The second major issue with an articulated segmentation is that it is all based around a respondent’s view of themselves as opposed to an impartial third party view of them. A respondent may say that eating healthily is important to them however if we looked at their shopping bills we might see that they buy a below average amount of fruit and vegetables.
By looking at consumers from the articulated angle we don’t see an accurate picture of their actions. Experiments in behavioral economics have routinely shown that the gap between our view of ourselves and the truth is wider than we think. A shining example of this is consumers’ understanding of mobile tariff usage; despite the myriad of different ways to track data usage, the vast majority overestimate how much it is that they use. For a complete picture of the individual we must take into account this discrepancy between perception and behavior. An example of this would be m-commerce; to identify the leading edge consumers you don’t want to look at those that say they are happy to make payments through their mobile you want to look at those that already do.
Consumers can report their behavior and this is often the most cost effective way of collecting behavioral data; however it needs to be done carefully to avoid the pitfalls above. Questions need clear parameters and to be strictly reporting as opposed to summarizing or predictive.
as opposed to;
A purely behavioral based segmentation however is also a floored concept because it does not acknowledge the importance of the idealized self. The idealized self is a product of our aspirations and these are what drive purchases. We may see ourselves as a bit of a foodie and so will be drawn to the look of the fridge advertised alongside bottles of wine and wheels of stilton. We convince ourselves that we definitely need the ambient section for storing Merlot at optimum temperature, even if our appliance rarely sees anything more adventurous than Carlsberg and Baby Bell. The aspirational self is a key part of the marketing and messaging value of segmentation. It is essential to understand the consumer not just from your own perspective but theirs’s as well.
For more information please contact Samuel Carter at email@example.com.
There’s no limit these days to the volume and type of data that companies use to improve their competitiveness. Much of this data is unique to the industry in question, but some market indicators such as purchasing power have nearly universal application. A measure of the population’s disposable income, purchasing power data is the primary benchmark for determining consumer potential. So why is this market indicator so valuable and versatile? Simply put, purchasing power shows companies and manufacturers where the population has sufficient disposable income to spend on retail purchases. And even more importantly, good purchasing power data shows how this disposable income varies throughout the entire market and at different regional levels such as municipalities and postcodes.
Let’s now look at how purchasing power data can help a consumer electronics retailer who sells products via chain stores throughout Europe. Optimally placing and managing these stores requires precise, up-to-date knowledge on how the product potential tracks across regional markets. The retailer happens to know that Europeans have roughly €9 trillion to spend in 2015, but this information alone is useless. This is where our purchasing power data comes into play. Our data offers a highly textured breakdown of the geographic distribution of this wealth. It’s not enough for our retailer to work with composite figures and rough averages, because actual purchasing power amounts fluctuate dramatically from country to country, municipality to municipality, and even postcode to postcode.
So where does the retailer start? A good first step is to assess the relative wealth in the areas around the chain stores. This has a direct effect on how the retailer should optimize the product mix for each location. A quick look at the data reveals that Liechtenstein is a veritable purchasing power dynamo, with 4.5 times the average disposable income. Our retailer has two stores in this area, so a good move would be to offer a larger selection of high-end products at these locations. If the two stores are not fully tapping the available potential, the retailer can consider opening up some additional stores in this country, strategically positioning them in municipalities and postcodes with especially high purchasing power.
The retailer also has stores in Central European markets, such as Poland, which has shown signs of rapid retail growth. But unfortunately the retailer’s stores in that country have not be able to capitalize on this trend. Another look at the data shows why: All of the retailer’s stores are in districts with below average purchasing power by Poland’s standards. The retailer now has several options. It can open stores in some of the districts with higher purchasing power, such as Sopot, Piaseczynski or Warsaw, the latter of which has almost 83 percent more purchasing power than the rest of the country. Alternatively, the retailer can adjust its product mix at the existing locations to better appeal to the surrounding demographic. Up until now, the retailer has been using the same or similar marketing campaigns for all of its stores. Using the purchasing power data, marketing campaigns and POS promotions can now be tailored on a store-by-store basis to the income level and purchase affinity of the nearby population.
The retailer can also use the purchasing power data to more objectively measure the performance of its stores in each of its active countries and regions. Previously, the retailer had no way of gauging what a good result was for the markets where its stores are located. For example, the retailer knew its best-performing store in France was in Paris and its worst performing in Pas-de-Calais. But this knowledge was meaningless without insight into how these performances relate to the market potential in those areas. Using the purchasing power data, the retailer discovered that Paris has an average per-capita purchasing power of €29,433 (more than twice the European average), while Pas-de-Calais has just €15,688. Thanks to these precise, regionally sensitive numbers, the retailer can more accurately gauge both individual store performance and how those performances compare as a percentage of the total local market.
Purchasing power data is the ideal foundational market indicator for users across all industries, from retail and real estate to automotive and tourism. Users can easily supplement purchasing power with additional market indicators, such as retail turnover potential and purchasing power for specific product lines. GfK Purchasing Power Europe is calculated annually for 42 European countries and provides comprehensive coverage down to the level of municipalities and postcodes, as well as data on inhabitants and households. GfK’s Geomarketing solutions also include digital maps and other market data that fit seamlessly with the purchasing power data.
For more information on GfK’s Geomarketing offering, visit www.gfk.com/solutions/geomarketing.
Some people have argued that the BBC’s role in the British Media has considerably diminished over the past few years, but as the organization still reaches 97% of the population every week, I believe it still has an important role to play. Furthermore, with competition from OTT services continually rising, old Auntie can’t afford to stand still and must ensure she retains her share of the market, especially among younger audiences.
Some key changes in the market. As well as an erosion of the amount of time generations are spending with TV and Radio, audiences now also want to be ‘in control’ of their content. Thinking about TV viewing specifically, viewers want to decide when they watch something, how they watch it (all episodes of a series in one sitting) and how they are going to share it with friends and family.
If we look at how Radio 1 was consumed 10 years ago, for example, the changes compared to today are remarkable. Where once shows were only listened to at specific times of the day, users can now choose to rewind bits of the show they missed, or just listen to it all again later; they can tune in to their favorite shows on the car radio, but they can also listen online through the app or the website (on a range of devices); podcasts are created on a daily basis and thousands of views are registered every day on the station’s YouTube channel.
Moving away from radio, the BBC has been also experimenting (successfully) with Netflix-style TV launches, making a whole series of TV shows available to its users in one go. For example, the launch of Car Share was met with millions of iPlayer requests to stream/download each episode of the series, a much higher audience volume than would have been expected had the show been released offline.
Many of the changes I’ve mentioned were incorporated by the BBC long before the majority of their competitors, so they have had time to refine their strategies, as well as providing the organization valuable learnings to take forward. But in my opinion, the most interesting move is how the BBC are using audience data to improve their services.
Chart Beat is a tool the BBC currently employs to analyze traffic data across all of the BBC’s websites in real time. The News and Current Affairs team monitor which stories are performing well (or not so well) on BBC News website, and how they can instantly re-arrange the webpages to increase audience engagement. The second tool that was talked about was MyBBC. This new service, using data made available by users being signed in across the BBCs platforms, will eventually provide audiences with tailored content that helps them unlock even more value from the BBC which, in the long run, will increase overall satisfaction and loyalty to the organization.
The BBC has previously anticipated industry changes and reacted by developing the necessary infrastructure to fully serve its audience. From what we have heard they are developing, and from seeing how they have adapted their delivery and content strategies in the past, I think we can be confident that the BBC will continue to evolve and find new ways to serve its existing audience, as well as finding new, innovative ways to serve the next generations of viewers, listeners, readers and browsers.
Niko Waesche (Global Industry Lead of Media and Entertainment @ GfK) and Nick North (Director of Audiences @ BBC) shared the presenting duties in the penultimate keynote speech of the GfK Future Consumer Summit 2015, speaking about the changes happening to the media landscape and the challenges this presents media companies of today. In the first part of the presentation, Niko focused in on the issues surrounding the industry as a whole (see part 1), while in the second half, Nick North explained the steps the BBC has taken to keep up with the ever-evolving consumer trends, and what plans the organization has to cope with changes in the future.