7 step brand planning in a big data worldquinta-feira, 19 de setembro de 2019
I’ve spent half my career working in advertising, and later brand strategy consulting. Brands were my first love. But how we think about building brand portfolios has changed. When I work with clients now, I have the luxury of supporting them with dramatically more data than before, and this has some implications for how we think about brand portfolios and brand planning looking to 2020 plans and beyond.
The need to build strong brands is more important than ever now. Brands are now everywhere, and there are too many of them. You see them when you are outside; when you type into a search engine, scroll through online retail platforms, watch streaming video (the evolved form of television?), read a newspaper or scroll carefree through our social media feeds.
How many brands (and sub-brands) do you need in your portfolio?
What are the bets you are making on different brands, with different investment and risk profiles, and varying time horizons?
In a world of big data and analytics, the brand portfolio can be optimised a “House of bets”, not a “House of brands”. Amidst all this proliferation, has the fundamental definition of a ‘brand’ changed? Unlike what we hear, it has not. A ‘brand’ stands for two things in the minds of the consumer – ‘Proposition’ and ‘Trust’, regardless of the product category or industry segment. The path to building a strong brand is dependent on creating a strong proposition and building trust. Brands are shorthand for 2 things: Proposition and trust. Whether thinking of a CPG brand in a supermarket or a T-shirt you’re buying via google shopping intermediated and curated.
But say in OTAs, hotel brands (trust, and proposition shorthand for what to expect site unseen, is the hotel hip or hooray?) matter more than direct booking (because you have to go the app or group website anyway). Say you’re on a retail platform, you’re committing to buy, or type “hair curlers” into a search engines, you want brands you recognise. If the fundamental definition of a brand has not changed, then what is 21st century brand planning? i.e.: Brand planning in a World of analytics and big data. It is nothing but a fact based approach towards brand building, with data and analytics at its heart. It is also not a sporadic and undisciplined use of data analytics but a principle that runs through the whole course of the brand planning process (as below):
- Defining the market structure, i.e.: a market map, typically of segments/ occasions
- Conducting segmentation identify need-based segments
- Aligning the brand architecture
- Optimising the brand portfolio
- Proposition development to cater to the needs of the segments
- Developing the marketing strategy (e.g. allocating marketing spends across the portfolio, maximising spend effectiveness)
- Developing the communications strategy
These are the 7 steps for brand planning now – let’s think about the role of analytics and data. Market structure is a great place to start. If you map the market, how many brands do you need? More brands gives you coverage of the market, but they can begin to overlap and lose efficiency. Too few brands might give you insufficient coverage of the market opportunity. But right sized brand bets against niches in the market map might make sense. (By the way, by the new rules of the market map, SKUs focused on different segments/ occasions <think single="" serve="" drinks="" convenience="" skus=""> surely behave as brands in themselves, not just “expressions” of brands. I always argued with my colleagues in brand strategy consulting that SKUs could sometimes indeed be brands, and I still think I’m right).
Harnessing the power of data analytics is crucial to ensure that the core of the brand planning process always remains fact-based. For example, it has become increasingly complex to define a market map for a brand. Consumption needs are now increasingly ‘occasion-driven’, which is being fuelled by brands adopting strategies to enhance the experience of using them. Because of this, the role and relationships between brands in a portfolio is more likely to be defined by how a consumer choses between brands on a specific occasion.
Occasion based consumption has created portfolio complexity. Given the new channel complexity, and how we generally think of market maps more defined by “occasion” now, the role and relationship of each brand your portfolio is not going to be so defined now, as the same consumer will choice different brands of your depending more now on occasion (and to some extent channel).
Leveraging the power of analytics on both structured and unstructured data allows brand planners to utilise the consumers’ voice as the principal guiding factor. From defining the market structure to creating communication strategies for different need-based segments, analytics ensures that brand building does not sway into a sea of unknowns and knee-jerk reactions.
Death of the house of brands? Because brands have to overlap more in an occasion-based model, and brand consolidation have happened for marketing efficiency, so we see more stretched brands, the “house of brands”, nicely targeting discrete cells of the market map, is surely a thing of the past. We’ll see more hybrid models of endorsed brands and merged brands. A common question is why is data so critical in the brand building process now compared to the past.
The answer lies in the increasing complexity of building a strong brand. This is leading to established brand building concepts to be challenged. For example, let’s consider the classic ‘Branded House’ vs. ‘House of Brands’ brand architecture frameworks. With experiential branding on the rise and the plethora of occasions where a brand needs to be a fit exploding, we are witnessing more ‘brand stretch’ scenarios.
This is leading to the whole House of Brands framework to be abandoned in the favour of ‘endorsed brands’ or ‘co-branding’. As needs and occasions become more fragmented, we are surely going to witness the emergence of more hybrid brand architecture frameworks going forward.
Analytics prowess enables organisations to break free of their legacy thinking shackles. Consumer needs-based segmentation, if done well, routinely identifies ‘white space’ opportunities for brands to enter into. With the new age, freer thinking around building brands, the phenomenon of a branded company is disappearing. Simply put, organisations don’t define brands anymore, but a single or a portfolio of brands defines an organisation. This allows new product categories to be entered into (e.g. either through brand stretch or innovation) and different price points to be covered.
By contrast, a branded company was a prisoner of its products, propositions and price points. Knowledge precedes effective decision-making. With data analytics, brand planners can have in-depth understanding of their consumer segments, which in turn allows for more opportunities for optimising propositions.
Remember the fact that an effective proposition is still a fundamental component of a brand. Death of the Branded Company and triumph of “Brand Led”? The identity of the brand supersedes the products for the “Brand led” business. The Brand led business can create/ enter distinct new product categories and cover different price levels.
By contrast the branded company was a prisoner of its products, proposition, and price point. Develop the brand positioning can be greatly helped by data. Brand positioning is space in the market map you want to park your tanks and the idea you want to own in the consumers’ minds - effective brand positioning strategy will maximize customer relevancy and marketing efficiency by matching the proposition to the segment bullseye. Data allows planners to maximise brand distinctiveness vs. competition, thereby stopping strategy being created and executed in a vacuum.
Brand strategy is incomplete without aspiring for ‘distinctiveness’, even though the journey might pass through achieving ‘differentiation’ on the way. Brand distinctiveness can be achieved over a period of time by following a few rules: Distinctive brands are unique, not predatory – They speak something different to what competitors are saying; a great segmentation can lead to a great proposition to match the needs of a segment, but what are you saying differently than the competition?
Distinctive brands don’t define themselves versus a competition – Consequently, communication strategies that emphasise phrases like “better than…”, “higher than…” etc., will only realise a short-term benefit but the brand will get defined by its competitor’s positioning Distinctive brands are resilient brands – Barring a few exceptions, barriers to entry is low in most product categories, resulting in quick competitor entry and reactions; it is critical that a brand is able to withstand these competitor moves across the price spectrum.
Distinctive brands have a surprisingly simple strategy – Distinctiveness is never the output of complexity and chaos but comes from simplicity; consequently to achieve distinctiveness, a brand should continuously strive to simplify its strategy The biggest challenge for any brand in the current economic climate is to achieve and sustain growth. Gradually marketing consensus is that you grow brands by increasing your proportion of light buyers (considered disloyal), which is nothing but increasing penetration.
Penetration is the new key to growth, and not loyalty. Strong analytical capabilities allow brand planners to have a continuous read on the revenue and profitability impact of a long-tail of light buyers vs. that of heavy buyers. This allows for growth strategies to be oriented towards segments where there is higher probability of acquiring light buyers. The new penetration objective of marketing helps us evolve our thinking about brands now. There’s a marketing consensus that you grow brands by increasing (not shortening!) your long tail of light buyers (and one assumes dis-loyal). Penetration is the new key to growth, not loyalty.
The acquisition of light buyers from different consumer segments requires the brand positioning to be sharp and effective. An effective brand positioning will strengthen the relevance of the brand and marketing efficiency by matching the proposition to the core needs driving a segment (“the bull’s-eye”). Developing an effective positioning is now both a creative and data-driven process, rather than being overtly reliant on one. This is where analytics helps us a lot. Focused marketing spend is what matters. Innovate, concentrate and dominate with the most resonant message for your target segment. Although 21st century brand planning may sound like a radically different idea, but any brand planner already effectively utilising analytical capabilities will say that it is not.
The difference is more that of a mindset change, rather than fundamentally change the brand planning process. The evolving nature of how a brand now moves through a customer decision journey makes data a critical component of the planning process. Data analytics unlocks value at each stage of the brand building process. This value comes in different shapes and forms – consumer needs understanding, brand perceptions and equity, performance at different stages of the brand funnel, acquisition / loyalty costs, sales / revenue / profitability analysis, customer life time value (CLTV), marketing mix modelling, ROI calculations etc. are myriad forms of value. As planners seek to build strong brands in today’s complex marketplaces, in-depth understanding of consumer, competition and category dynamics will be the keys to success.
Those who are able to leverage the power of data better will be able to use these keys quicker to unlock the gates of progress. There is now a multi-horizon Brand portfolio bet. Not an x-y plot of speed to value, but a portfolio of small, large, short, long term, in rental, transformative bets. It’s not a 45 degree line, but a mixed risk portfolio play over different timescales for each brand. The “House of brands” has become a “House of bets”…
The new brand portfolio is a collection of distinct brands operating under different time horizons, investment levels, and risk profiles, underwritten by the power of big data analytics.
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