Customer experience (CX) is the new marketing battlefront in today’s ultra-competitive times. This makes it vital for businesses and enterprises to give customers more than they expected to get more than they thought possible!
An E-Consultancy and Ensighten survey of more than 600 companies worldwide revealed that “nearly all (96 percent) surveyed companies deem customer experience optimization important”, with more than two in five companies (41 percent) stating it was “a high priority for their organization.”
Good quality customer data can help with better KYC, better customer behaviour, and customer propensity analysis. It also ensures better consistency in customer experience, especially in Omnichannel functions of a retail enterprise.
Which customer would not appreciate their m-commerce account being a reflection of their ecommerce profile so that all experiences are similar? All this is possible only with correct, consistent, and complete customer data.
Apart from enhancing the experience for customers, data-led marketing has many other benefits: improved quality of marketing, better returns on marketing investment, lower costs, and higher revenues.
The paybacks of quality data include superior engagement with customers, increased collaboration between departments, improved reaction time to market changes, and informed, smarter decision-making – a must in today’s fast-evolving retail landscape.
But, not all data is created equal.
- Harvard Business Review states that only 3% of companies’ data meets basic quality standards
- Deloitte research shows that 71% of consumer data erroneous
- Gartner reveals that poor quality data costs businesses $15 million on average
Dirty data is basically data that contains inaccurate, erroneous information. It can be classified into various types: duplicate, outdated, insecure, incomplete, inaccurate, incorrect, inconsistent, and hoarded. The usual culprits to blame are human error, poor interdepartmental communication, and most importantly, because of an inadequate data strategy.
Why companies need quality data
Business organizations that focus on the quality of their data reap rich rewards. Research from the University of Texas shows that increasing data usability by even 10% can boost revenue for Fortune 1000 companies by more than $2 billion per year.
Data silos and outdated information can keep companies from gleaning the insights they need to deliver superior customer experiences. With high-quality data, companies are more agile, productive, and competitive.
Good data quality lets you:
- Spot trends and predict outcomes
Analysing quality data can help spot trends and identify new customer behaviour patterns. On average, most organizations use 36 different data sources, with little to low integration. Up to 88% of available customer data tends to be ignored. Data management is vital to ensure quality for good data leading to employees spending 33% less time looking for data and 33% more time acting on it. Quality data can help businesses reallocate resources to tap new revenue-generating opportunities. Specific, detailed data ensures precise analysis of emerging trends and potential outcomes.
- Deliver more effective campaigns
Robust data is more effective and amps up your ability to send the right message to your target audience. Quality data can help connect all customer-specific dots, and hone in on the interests and preferences of your top customers and potential leads – ultimately increasing your client list.
- Reduce costs and increase efficiencies
Mining and making sense of data can help businesses cut back on costs and improve efficiencies. The right data sets can help companies pick up the pace on projects and make changes much more quickly. They also ensure operational efficiency by using less resources.
- Increase customer engagement and retention
Having enough data on your customers helps you know them better. This lets you recognize their wants and needs, augment or create demand, personalize messaging, deliver memorable experiences, and build and strengthen customer relationships. Parsing all available customer data can help create personalized journeys and superior experiences.
A study released by the Economist Intelligence Unit (EIU) said 71% of respondents in a survey said their response to a bad experience was to stop doing business with the company. An exceptional experience led them to use the company’s products/services again (69%), telling friends and family (51%), and commenting on a social media site (23%).
The impact of dirty data on CX
Dirty data costs companies worldwide anywhere between 15% and 25% of revenue.
More importantly, dirty data leads to loss of goodwill, increases customer churn, and derails new customer acquisition. Using outdated data to reach out to customers can repel them instead of attracting them to your offering.
That’s not all.
Poor quality data lacks credibility – only 16% of business executives believe in its accuracy. This means that users must spend extra time to confirm its accuracy. The introduction of another manual process translates into mounting errors, inaccuracies, and inconsistencies.
Clearly, garbage in, garbage out — if companies can’t rely on their own data, there’s no surety about the accuracy and reliability of initiatives based on this information.
The irony is that most companies may not even know that they are using dirty data; by the time they realize this, the losses pile up. Productivity is adversely affected, with data scientists and knowledge workers spending way more time cleaning, normalizing, and organizing data.
It is vital to constantly monitor data quality through robust data governance mechanisms as an ongoing process. For excessive rework on tactically improving data quality will only result in more data errors.
Dirty data has the same effect on your business that junk food has on your body – you think you’re fuelling it to run smoothly and keep it in top gear, but you’re doing serious damage and may hit numerous roadblocks.
So, how can you ensure rich, quality data?
In today’s world, where most businesses and companies are taking a data-first approach, it is imperative that data is rich, robust, and quality. Disparate systems, sources, and processes lead to inconsistency in data collection and spotlight the need to clean and standardize data.
According to McKinsey, organizations are leaning on data insights more than ever to cope with the pandemic’s fallout, with industry spending on data-related costs expected to increase on average by nearly 50% over 2019-21 as compared to 2016-18.
Kate Leggett, Principal Analyst at Forrester Research, in an article suggests a five-pronged process for developers to begin the data quality journey:
- Don’t view poor data quality as a disease
- Be specific about dirty data’s impact on business effectiveness
- Scope the data quality problem
- Pick the right business process to support
- Define recognizable success by improving data quality
Gathering good quality data on various customer journeys can help an organization understand customers better, and build timely, and relevant and immediate suggestions/recommendations.
But gathering data isn’t enough.
It is important to ensure data cleaning, or cleansing, to create a rich repository of relevant information. This process involves identifying and replacing incomplete, inaccurate, irrelevant, or problematic data and records. The end result should be data sets that are consistent and free of any errors – for the cost of poor data increases exponentially according to the 1-10-100 quality principle.
Data quality is an ongoing challenge and can be tackled by leveraging regular assessments, following best data hygiene practices, and ensuring comprehensive data management.
As more companies recognize how much customer happiness impacts the bottom line, customer experience has grabbed the spotlight. Experts say this competitive advantage is likely to overtake price and product as the primary brand differentiator.
Customer experience research, consulting, and training firm Temkin Group agrees, stating that loyal customers are “5x as likely to repurchase, 5x as likely to forgive, 7x as likely to try a new offering, and 4x as likely to refer”.
The numbers do the talking and it’s critical that businesses tap the biggest weapon in their arsenal: quality data.
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