Seeing the Bigger Picture: The Importance of Contextual Data in Brand Tracking

We are living in a relationship economy. Customers expect brands to understand their needs, and personalise their products, services and communication accordingly. More than half of marketers surveyed^[1] say that they need intent data to enable more accurate personalization, or to create better and more engaging brand-customer experiences.

However, collecting and collating data from disparate sources proves a real challenge. Less than half (45%) of brands^[2] have a single view of data across all channels. Without consolidated, contextual data - and the ability to tailor brand communication, offers and products to customers - it becomes far harder to build genuine, long-lasting consumer relationships or to launch personalised, relevant marketing campaigns.

Unifying Brand Tracking: Breaking Down Data Silos for Unified Customer Insights

A data silo is a collection of data that is isolated from other data within an organisation. This isolation can occur due to various factors, such as different departments or systems collecting and storing data independently. It can happen extremely easily. For example, your brand tracking data is likely handled by the insights team. But your social media is handled by an entirely different team with different skill sets, KPIs, and processes that probably don’t reside within the insights department. You also receive a regular influx of information through your website analytics, which likely sits with the marketing, digital or performance team.

All of these teams have insight into one piece of the puzzle. Your brand tracking team follows awareness and brand mentions, your social media team sees complaints and compliments, and your website team gets good insight into the products that actually sell or draw interest. But no one has a complete, holistic view of the brand’s performance. And that becomes dangerous:

50% of customers^[3] will switch to a competitor after a single bad or unsatisfactory experience. If customer interactions are uninformed, customer satisfaction will suffer.
54% of organisations ^[3] say that fragmented and siloed data is the biggest reason they haven’t been able to leverage data as fragmentation leads to incomplete customer profiles.
Only 22% of companies^[4] believe that teams share data well, which impacts the ability of support agents and marketers to do their jobs well.
71% of customers^[5] expected personalised experiences, which is difficult to deliver without access to comprehensive information.

While some organisations have dedicated Business Intelligence (BI) and analytics teams working with business intelligence platforms to combat data fragmentation, the process can often be complex and resource-intensive. These teams may employ data lakes and big data analytics to piece together actionable customer insights from various silos. However, the approaches to structure fragmented data can be overly complicated for the specific needs of brand tracking and may not provide the most efficient or cost-effective solution for measuring brand performance.

Integrating Contextual Data in Brand Tracking

Even if marketing teams have brand tracking data available, it can be challenging to draw strategic business insights when it’s accessed in isolation and without context. For example, metrics like spontaneous brand awareness and consideration may fluctuate due to a number of different factors. Perhaps there has been a defect that has led to bad reviews. Maybe the advertising budget has increased leading to a spike in awareness, or a new competitor has entered the fray with increased ad spend of their own. Without additional context, the team can’t definitively make a call or take action.

To fully gain a more comprehensive understanding of brand performance, it's essential to consider contextual data. This includes information such as:

Media Spend: Data on marketing expenditures, also known as share of voice, provides insights into how much a brand is investing in advertising compared to competitors.
Customer Satisfaction: Metrics like Net Promoter Score (NPS) can reveal customer loyalty, satisfaction, and likelihood to recommend the brand.
Macroeconomic Indicators: Factors like inflation rates, employment rates, and consumer confidence can influence overall market conditions and impact brand performance.

Manually integrating these siloed data sources can be a time-consuming and labour-intensive process. Teams have to gather data from various departments, ensure consistency, and then analyse it together. This can be slow and inefficient, especially if there are delays in data availability or a lack of cooperation among teams.

To overcome these challenges, organisations should consider implementing automated data integration and brand analysis tools through a more holistic brand intelligence platform. These tools can streamline the process of collecting, cleaning, and combining data from different sources, providing a more efficient and accurate way to gain actionable insights into brand performance.

Benefits of Automated Integration of Contextual Data for Brand Tracking

Automating the integration of contextual data with brand tracking data offers numerous advantages. By combining these data sources into a single brand tracking dashboard, organisations can gain a richer, more comprehensive, and more accurate view of brand performance. This enhanced understanding enables faster and more informed decision-making because:

Combining data sources provides a deeper understanding of brand performance and identifies trends that might be missed when examining data in isolation.
Automated integration eliminates the need for manual data collection and processing, allowing for quicker access to contextual data. This is particularly valuable in crisis situations where rapid decision-making is essential.
When contextual data is readily available, teams are more likely to consider the broader picture and avoid making decisions based on isolated metrics.

Enlyta Insights is a powerful brand intelligence platform that can harmonise data from various sources, including brand tracking data and contextual data. It allows for the overlay of metrics with different frequencies, such as monthly brand tracking and quarterly economic data, on a single chart.

Enlyta supports various data formats, including APIs, Excel files, and CSV files, making it easy for clients to provide their data. By using APIs, Enlyta can integrate data in real-time or near real-time, ensuring that data are always up-to-date. Brand Performance measuring teams have the ability to curate and control which metrics are included from different data sources, ensuring that only relevant data is analysed. Enlyta's automated integration capabilities provide instant access to contextual data, enabling teams to make quick and informed decisions. With Enlyta’s ‘Chart Builder’ they can plot any metric from any source alongside any other on the same chart, to reveal the holistic picture of performance, in the right context. Organisations can streamline their brand tracking processes, gain valuable insights, and make data-driven decisions that drive business success - using a single, unified tool.

Conclusion

Fragmented data only paints a portion of the picture. Brands that need a comprehensive understanding of their brand health and performance have to find a way to integrate contextual data with brand tracking data. By breaking down data silos and automating the integration process, you can discover hidden brand analysis trends and patterns that might be missed when examining data in isolation and develop more effective brand marketing strategies based on insights from a complete view of the market.

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