Chief revenue officers of large corporations are often faced with striking the proper balance amongst short-term revenue chases and long-term brand equity development strategies. Due to the rise of effective marketing intelligence and Big Data, this position has become much more difficult to perform. What are the obstacles that brand equity faces when data becomes more plentiful while also getting more precise at the same time?
Recent technological advancements, such as artificial intelligence-powered predictive analytics, have enabled brand strategists to forecast which marketing offers their clients would find enticing. Large amounts of information about people’s purchasing habits and transaction histories may be gathered by analysts, who can use this approach to determine future consumers.
In this post, big data consulting services define brand equity and discuss the influence that data analytics may have on its value and influence. Afterwards, we’ll go through some of the most major ways that incorrect data may have a negative impact on brand equity.
What is the definition of brand equity?
In marketing, brand equity refers to the amount of influence a brand name has on the minds of customers, as well as the benefit of having a brand that is easily recognised and well-remembered. Organizations build brand equity by providing customers with favourable experiences that encourage them to continue buying from them rather than from rivals that manufacture identical items. This is accomplished via the creation of awareness campaigns that relate to the values of target customers, the fulfilment of promises and qualifications when consumers use the product, and the promotion of loyalty and retention initiatives.
How Inaccurate Data Can Affect Brand Equity
Because your company has established a strong Brand Equity for its existing line of products and services, it will be easier to introduce the new line of offerings to the same target market and group, as well as to previously untapped markets and consumers, as a result of the strong legacy that has been cultivated over time.
For example, data analysts often use the phrase “garbage in, garbage out” to characterise the impact of faulty data on the outputs of brand equity analytics. In other words, when faulty data is used to generate analytics results, the insights derived from it are very inaccurate and should be avoided. If the insights are untrustworthy, it will have an impact on the brand’s image and consumer impression at the end of the day.
1. Errors in the use of programmatic advertising
While brand-building tactics were formerly dependent only on intuition, the methods involved in brand-building have altered dramatically as a result of the development of big data analytics. Numerous phases of a brand’s advertising campaign, including the procurement of media, are becoming more automated in today’s world. The vast majority of consumer data that may be used to extract insights is unprocessed and unusable in any way.
2. The Implications for the Customer Experience
Customers are the ones who bear the direct consequences of any conversation in which brands and goods are engaged. As a consequence, inaccurate data regarding consumer behaviour might lead to the development of goods and branding that devalue the customer experience.
3. Advertising analytics are aggressive
Presently, data analytics on a product, a brand, and an organisation are all accessible on centralised platforms in the current market environment. Promotional analytics, which makes use of this data, offers marketing strategists the required insights to assist them in their promotional marketing efforts. On a long-term basis, this form of marketing delivers rewards to the company involved.
4. Brand Transformation in low pace
Considering that brand equity is comprised of both real and intangible components, assessing brand equity from an objective position might prove to be challenging. In the following part, we’ll go over some of the most essential indicators and elements to consider when seeking to reach a consensus on the value of your company’s brand.
When a corporation chooses to radically reinvent its branding efforts, data is a key commodity to have at its disposal. For brand transformation plans to be effective, high levels of data quality must be maintained throughout their execution. Over the next five years, big data investment will only expand and accelerate at a faster pace for global advertising firms.
Concluding Remarks
Brand Equity is widely recognised as an important aspect in both marketing and business strategy since it is based on the notion that brands are assets that promote long-term company success. It is not only tactically advantageous to produce short-term sales, but it is also strategically advantageous to the creation of long-term value for an organisation when it comes to brand equity.
This article was contributed by Evan Gilbort.
