What is a brand?
A brand can be represented in different forms, from product features (e.g. speed, design), brand name (e.g. Apple, Dell), to consumer’s general affection and perception of products (e.g. sophistication, cheerfulness). Marketers analyse brands to understand the customer relationship and build brand equity to achieve tangible financial gains.
What is brand architecture?
Brand Architecture is created to maintain the consistency of marketing strategies which must be aligned with the company’s brand. Marketers constantly refer to company’s brand architecture to deliver a unified view of company’s products and services to consumers across all the marketing campaigns. Brand architecture consists of the following elements:
- Brand Core/ Essence e.g. Freedom, Youth, Cutting-edge
- Brand Personality e.g. Friendly, Fun, Down-to-Earth, Quirky
- Emotional Benefits e.g. Less Hassel, Try to Please
- Product Benefits e.g. Fast, Reliable, Convenient
- Product Attributes e.g. Point-to-Point, Low Price, Delicious
Inter-brand Valuation Model
Develop by Interbrand, Interbrand Valuation Model takes into accounts different aspects of an organization to create a holistic and accurate valuation of the company’s brand. The approach highlights the impacts of brands on the relevant stakeholders: customers, employees, and investors.
- Financial Analysis: measures the overall financial strength of an organisation
- Role of Brand: measures the portion of purchase decision attributable to the brand, as opposed to other factors
- Brand Strength: measures the ability of the brand to create loyalty and, sustainable demand and profit into the future
Brand Asset Valuator
Developed by Young and Rubicam, Brand Asset Valuator is used as a complimentary tool with the Interbrand valuation model to provide management an overall view of the organisation’s brand asset. The valuator consists of four key elements of a product/service:
- Differentiation: How the product is differentiated in the market
- Relevance: How appropriate the brand is to consumers
- Esteem: How well regarded the brand is
- Knowledge: How much the product/service is perceived and understood by consumers
Differentiation and Relevance attributes to the Brand Strength/Vitality which refers to the current and future growth potential a brand holds. Esteem and Knowledge together indicate the Brand Stature or the power of a brand. Based on the Brand Asset Valuator, marketers will be able to identify and position their organizations into the appropriate Brand Strength vs. Brand Stature quadrant:
- 1st quadrant (top left): high differentiation, low knowledge
- Strategic priority: focus on marketing tactics to enhance customer awareness of products/services
- 2nd quadrant (top right): high differentiation, high knowledge
- Strategic priority: perform targeted marketing to improve retention rate and maintain product relevancy to consumers
- 3rd quadrant (bottom left): low differentiation, low knowledge
- Strategic priority: target products/services to the early adopter consumer group while enhancing product quality and features
- 4th quadrant (bottom right): high knowledge, low differentiation: consumers have high awareness of the brand but it has failed to differentiate itself from competitors
- Strategic priority: improve product quality and features; focus on product innovation
Revenue Premium as a Measure of Brand Equity
To measure the brand equity, the capitalised value of brand, we can value based on the revenue premium which a brand earns in a market over a private label. This method is based on 2 assumptions:
- Brands make optimal marketing decisions
- Demand faced by a private label is equal to the demand faced by the branded product without the brand name
Brand Equity = Revenue Premium – Additional Variable Cost
PV of Brand Equity = (Revenue Premium – Additional Variable Cost) * Long-term multiplier
Revenue Premium = Brand’s revenue share – Private Label’s revenue share
Revenue share = Price * Market share * Unit sales
Additional Variable Cost = Differences in market share * (1 – Margin % of private label) * Price of Market Label * Unit Sales of Brand
Long-term multiplier (LTM) = (1 + d) / (1 + d – r)
d: discount rate
r: stability factor
Brand equity, when valued based on revenue premium, is based on the fundamental premise that the difference in net profit of a branded product and an unbranded product is solely due to the brand name, i.e. the branded and unbranded products are identical.
Conjoint Analysis is a marketing technique that quantifies product attributes’ values in term of consumer surplus. From this, marketers can determine trade-offs that consumers are willing to make among the different attributes or features of the product. To perform conjoint analysis, firstly analysts need to come up with appropriate experimental design by identifying all attributes and the possible values of the attributes that will be tested. Next, data input for conjoint analysis is collected through various channels, typically through consumer surveys. In the surveys, consumers are presented with hypothetical product profiles and questioned for attribute-level utilities for the respective product features.
A sample of the conjoint analysis output is presented as below:
The utilities is equal to the average consumer preferences for the corresponding levels of an attribute. In each attribute, the utilities are scaled such that they add up to zero. From this analysis result, marketers can apply into the follow areas:
Assuming we are analysing two attributes A and B to understand what trade-offs between these attributes customer would be willing to have. For example, the utility difference between Level 1 and 2 of attribute B is u where Level 1 utility is higher than Level 2 utility. The current product has features A1 and B2. To change product feature B from Level 2 to level 1, we must reduce the utility of attribute A so that consumer will just be as happy as before the change is implemented. The reduction of utility is exactly equal to the difference between level 1 and 2 utilities of attribute B. To identify the new level for attribute A, we can assume a linear relationship between attribute A and utility and estimate the new level of attribute A by performing a linear interpolation:
new utility of attribute A = current utility of A1 – u
Assuming level A2 of attribute A is the level which has utility the most closest to the new utility of attribute A
New level Ai of attribute A = A1 + (current utility of A1 – new utility of attribute A)/(|current utility of A1 – utility of A2|) * (|A1 – A2|)
Predicting Market Share
Another common application of conjoint analysis is to predict market share based on consumer utility. There are two assumptions to take into account:
- The set of products that consumer is likely to consider when making a selection must be known to the company
- All the utilities of attributes and attribute-levels of these competitive products must be included in the experiment
Market share prediction relies on the use of a multinational logit model.
As one of the common application of analytics, cluster analysis and segmentation has been used by marketers to study characteristics of consumers based on different socio-economic attributes. With the advance of Big Data and the technology to collect data from a wider channels, marketers have expanded the use of data inputs to profile the customer base more effectively. The cluster analysis inputs can include following customers’ attributes:
- demographic characteristics
- desired benefits from product/services
- past-purchase and product-use behaviours
In the context of marketing analytics, a cluster is a group of customers sharing the same set of characteristics. Customers who are in the same cluster are homogenous while those belong to different clusters are dissimilar to each other. The input to cluster analysis is a measure of distance between individuals which indicates their similarity on the segmentation variables. Euclidean distance between user A and B is represented as:
A common cluster analysis procedure, K-mean clustering algorithm is as follows:
- Choose the number of clusters, k
- Generate k random points as cluster centroids
- Assign each point to the nearest cluster centroid
- Recompute the new cluster centroid
- Repeat step 3 and 4 until some convergence criterion is met
A critical part of K-mean clustering algorithm is to choose an appropriate number of clusters, k. A well-known method is to identify the elbow criterion. Elbow criterion is identified through the plot of the ratio of the within-cluster variance to between-cluster variance against the number of clusters. As the number of clusters increase, the ratio is expected to keep decreasing. However, the margin gain from adding an additional cluster will drop to be unsubstantial in which the elbow point is identified.
A key metric widely used by data analysts is price elasticity. Price elasticity is the ratio of change in quantity demanded and the change in price of product. Price elasticity shows how sensitive sales are to changes in price.
Similarly, advertising elasticity can be defined as followed:
It is important to connect regression analysis to business decisions by computing the economic significance. For example, the advertising elasticity of a product is 1.42 i.e. a unit increase in number of promotions increases units purchased by 1.42. Assume that gross profit per unit is $5, cost of promotion is $0.50. The economic significance is:
Profit = (units purchased x gross profit) – (cost of promotion * number of promotions) = (1.42 x 5 – 0.50 * 1) = 6.6