Big data is no longer big news. Everyone is using it. Or at least, making an attempt to incorporate some data-driven practices into their marketing setup. The problem? Data has become massively complex and encompassing. Contextualising insights, obtained from a multitude of sources, requires an expert eye.
But hiring a data scientist can seem a steep investment. So, should you consider taking the plunge? Yes, if you can relate to any of the following problems:
You cannot measure and attribute your marketing ROI
Business value comes as a direct result of quantifying the expected outcomes from your marketing campaigns. However, when dealing with scattered and siloed marketing data, it's easy to misinterpret what your data is trying to tell you.
Only 21% of marketers use analytics to measure marketing ROI for all marketing engagement. We can assume that most marketers choose to evaluate a selected few activities, but again - there are a lot of blind spots left unattended. When it comes to content marketing, 47% of B2B marketers cannot measure the exact ROI. Further, 44% of businesses struggle to estimate social media marketing ROI and quantify the campaign results.
Data science enables you to capture and even predict those elusive numbers. You can choose to analyse any number of campaigns on a granular level so that you can connect specific insights to marketing challenges and outcomes.
For instance, you can deploy algorithms to gather customers' multiple identifiers across different channels (email address, data for cookies, phone number etc.) into a single customer profile. Such profiles could then be segmented into smaller groups and continuously updated as new information becomes available eg. The customer's response to certain email campaigns or personalised content marketing digest. Over time, your system will be able to identify lookalike groups of prospects that are likely to respond well to certain types of advertising.
The end result is that you are no longer second-guessing your ROI. You know exactly what will impact it and what results you can expect when you apply action Y to audience Z.
You struggle to connect with your customers at crucial points
Customer journeys are no longer as simple as - "saw a product", "bought it", "placed an order". Instead, they behave as if they are exploring a maze - make some u-turns, wander off and stop all of a sudden. Your job is to guide them towards the right exit - your offer.
Google has recently identified four new micro-moments, shaping how consumers now interact with information and brands:
1. "I-want-to-know" moments - 66% of smartphones users pick up their gadgets to look up something they saw in a TV commercial.
2. "I-want-to-go" moments - 82% of smartphones users look up a local business before visiting.
3. "I-want-to-do" moments - 91% grab their smartphones for ideas when doing a task.
4. "I-want-to-buy" moments - 82% of users consult their smartphones in-store when deciding on a purchase.
Staying on top of all their intentions is nearly impossible if done manually. Data science can help you locate the current touch points with your customers, attribute them to a specific stage of the customer journey: pre-purchase, purchase and post-purchase - and help identify the missed opportunities for connection (based on available data).
For example, a lot of shoppers prefer to place orders online to minimise the risk of not finding the product they want when visiting a nearby store. So how do you bring more customers indoors? Macys.com found an interesting solution. The retailer re-targets nearby customers with local inventory ads when they are in a nearby area. Such personalised ads display a pair of shoes in the size and colour the customer searched for earlier and increases their willingness to visit a store and make a purchase that day.
Data science can help you identify such micro-moments and conduct real-time, personalised marketing experiments, based on a multitude of different touchpoints.
You want to remain competitive without hiring more people
Staff costs can be crippling for growing companies. Data science can increase your teams' performance without multiplying the number of desks in the office. Some common marketing processes you can outsource to the algorithms are:
1. Advanced lead scoring. Your team will focus on converting the top 5% prospects, selected by the algorithm, rather than waste time on combing through a list of some 10,000 names.
2. Prioritised marketing action. Machine learning tools can help your team remain focused on what matters most, instead of wasting time on low-value chores.
3. Automatic campaign management. Pause, fine-tune and scrap marketing campaigns that yield low results.
4. Dynamic pricing. Develop models that rely on data-backed variables to adjust prices in real time instead of relying on hunches.
5. Better personalisation. Implement intimate strategies without spending time second-guessing customers' intentions or responses to your pitch.
Your ability to capitalise on data is crucial to your business success. Analytics tools are handy as they provide you with data to crunch, albeit they cannot tell you what your next big marketing idea should be. They can only tell you when that idea is working or not. Data science, on the contrary, can help spell out the "what", "how" and "why" of each marketing action you have planned.