Chief revenue officers of big brands often are tasked with finding the right balance between short-term revenue pursuits and long-term brand equity building. The emergence of advanced marketing analytics and Big Data is making this job much more challenging. As data is becoming more voluminous and yet more precise, what are the challenges it poses to brand equity?
Recent innovations such as AI-powered predictive analytics have allowed brand strategists to gauge what promotional offers customers will find appealing. With big data, analysts are able to gather tons of information about people's buying patterns and transaction histories to identify potential customers.
In this article, we start by defining what brand equity is and what effect data analytics can have on its impact. We then discuss the most significant ways in which bad data can affect brand equity negatively.
What Is Brand Equity And Its Relation To Data Analytics?
The simplest concepts about buying behavior dictate that customers will buy a product that they recognize and trust. Brand equity is the value that a strong brand name and public perception add to a product or a brand in its entirety. Good brand equity usually directly translates to increased revenue and a better future hold on the buyer base.
Data scientists working towards promotional analytics for market-leader products know that brand equity is everything for them. As near-term sales can be precisely engineered with Big Data these days, the right information gathering practices need to be employed to justify branding investments.
Earlier, it was easier to defend brand-building expenditure with distant and indefinite future payoffs. But that is not the case as data has become far more granular as has the approach employed to analyze this data. Redundancies in the data-related customer buying patterns or online transactions can lead to major pitfalls for long-term brand equity.
How Bad Data Can Impact Brand Equity
Data analysts often use the term 'garbage in, garbage out' to describe the effect of bad data on brand equity's analytics outcomes. In other words, when bad data is utilized to produce analytics outcomes the insights observed from it are highly unreliable. If the insights are unreliable, at the end of the day it would affect brand image and customer perception.
Let us take a detailed look at the ways in which bad data affects the following facets of brand equity:
1)Glitches In Programmatic Advertising
While brand-building strategies were formerly based on plain intuition, the practices involved in it have drastically changed with the advent of data analytics. Several steps of the advertising campaign for a brand are now becoming increasingly automated including the purchase of media. The majority of customer data available to derive insights are unrefined and unactionable.
Analysts helming advertising campaigns are usually supplied with third-party data, which is essentially data around customer demographics. Automation for finding correlations and identifiers in this data needs human supervision, without which the insights derived would be all over the place. With unactionable data, this task becomes doubly challenging. First-party data based on transactional history and user preferences, collected by your own organization should be prioritized to minimize bad programmatic advertising.
2)Impact On Customer Experience
When brands and products are involved in any discussion, the immediate impact of it all is borne by the customer. So bad data about consumer behavior could result in products and branding that devaluate the customer experience. And the prevalence of bad data is high - Deloitte estimates that 71% of the available demographic data is erroneous and misleading in nature.
Customer profiles created from demographic data are used for advertising, driving growth initiatives as well as customer initiatives. Apart from that this data is also used to drive inbound customer experiences, outbound targeted marketing as well as omnichannel strategies all collectively forming brand perception at the customers' end. Through data cleansing and the foremost purchase of good data from third parties, these components can be ensured to be glitch-free.
3)Excessive Promotional Analytics
In the present-day market scenario, data analytics about the product, brand, and organization are all available on centralized platforms. Promotional analytics using this data provides marketing strategists with the necessary insights to help with promotional marketing. This type of marketing pays the organization in dividends over the long term.
However, bad data can lead to redundancies while implementing promotional marketing drives. The data is not able to tell the analyst anything about the consumer logic for designing the most effective promotion. Such promotional campaigns can drive sales for a short duration but wean out in the long run as the precision of when to engage weakens over time.
4)Overspending
Capital expenditure into digital marketing has been on the rise due to increasing digitalization trends. The display ad spending in the digital sphere is usually done through programmatic channels driven by data analytics. Bad data can lead to an unhealthy concentration of ad spending towards low-yield branding strategies.
Feedback loops in programmatic ad channels tend to go against the principle behind ad spending. This principle calls for the right ad to the right person at the right time. Bad data used for targeting your ads could lead to less relevant ads reaching the wrong audiences altogether. These are supposedly the biggest sources of revenue loss in a programmatic ad channel.
5)Slow Brand Transformation Efforts
Data is a critical asset when a company decides to completely innovate over its branding efforts. High levels of data quality are necessary for the successful implementation of brand transformation strategies. Data investment is only going to increase and accelerate over the next five years for global ad enterprises.
When bad data slips into the analytics procedures for brand transformation, it retards the process very badly. The overall valuation of a product suffers heavily when its brand image is not altered from time to time. So optimization on returns is all but impossible with this slowdown in brand equity stabilization. Therefore companies are looking for better alternatives continuously which bad data does not help with very much.
ALSO READ: Three Pillars of Branding in a Tech Driven World
Ideal Brand Equity Demands Clean Data Practices
If brands do not have an effective data management strategy in place it can lead to lots of unnecessary retroactive effort and costs. Companies investing inefficient data management at scale can hope for good brand equity and smooth revenue growth. To know how you can ensure optimum brand equity with clean data practices, explore Daffodil Software's end-to-end Data Management Services.
