Big Data & Small Businesses: A Careful Collaboration

During this era of rapid development and automation, data collection is increasingly important for businesses. Data helps companies evolve by fueling artificial intelligence, improving decision-making processes and personalizing marketing strategies based on audience activity, preferences and more.

Small and medium-sized businesses (SMBs) might think they’ll encounter difficulties tapping into big data resources, but that doesn’t mean those resources are inaccessible. This article will cover how these brands can collect the data, which tools to use for analysis and when personalization pays off, while explaining the legal limitations.

Practical data gathering and use

It is true that large companies have a head start in data collection, as they have more resources to organize and securely store data for analysis. But this doesn’t mean that small businesses cannot take advantage of the data they already have, making the most of what already exists.

While the big names can buy data from third parties, small companies often have to rely on their own activity and collection efforts to obtain their data. But how can they do that? The following list will enumerate only a part of the possible sources that could help brands collect necessary data:

• Website analytics;
• Customer engagement patterns and transactions;
• Sales trends;
• Email marketing results;
• Social media interactions;
• User-provided data through questionnaires, forms and surveys.

Once the brand has this information stored, the administrative team must organize it in a helpful way. Structured data is easy to analyze, providing clear objectives and helping to set the operational strategy for the future. The analysis provides real-time insights into customer preferences, cuts costs and reduces inefficiencies and improves quality control, customer experience and marketing results.

Personalization: When is it helpful?

A part of these improvements results from personalization. The data analysis helps in that process, as the overall experience becomes better aligned with the customer’s wishes and needs. This could lead to a more attractive product that better represents the target audience and brings in new customers more efficiently.

To better illustrate this, let’s take an example of marketing strategies in casinos. A platform that uses its collected data to understand what its customers prefer can give out a $10 no deposit bonus to the user who prefers this type of offer. The operator could even provide an instant game recommendation system based on the player’s activity, so they know which games to play and how to use the benefits they receive.

While this personalization is useful in some cases, it can easily become invasive and frustrating if the data analysis is poorly timed or produces incorrect assumptions. In these situations, personalization can turn into a waste of budget. Companies must use the data analysis to its full potential to remain relevant, yet not turn into surveillance machines.

Compliance limits to behavioral targeting

Behavioral targeting, which helps in these personalized marketing strategies, can easily become a moral dilemma. This is why many authorities have put in place legal boundaries that can limit businesses from abusing the data they collect and/or selling it to third parties.

In Europe, the General Data Protection Regulation (GDPR) is the strictest privacy and security law, designed to protect user data. Breaches of these boundaries can lead to harsh fines for companies, whether or not they are in the EU, since any brand that targets EU audiences has to comply with this legislation.

Small businesses have to pay closer attention to these regulations, as the repercussions could prove detrimental to the company. They must maintain a high level of transparency, informing their customers how they will use the data obtained and seeking their explicit consent. This way, the brand builds trust with its customer base.

Conclusion

SMBs can use big data to develop their operational objectives and marketing strategies. They just have to be careful how they incorporate data analysis into their methods while maintaining transparency with their clients.

The customers need to know what data the company collects and how it will use it. This collaboration between clients’ data (given with explicit consent) and businesses can lead to more effective marketing approaches, efficient customer acquisition and higher retention rates.