With the arrival of 2020, it’s time to analyze the past, assess the present and embrace the future of data. Facts, figures and thought leaders agree: If you don’t aggressively embrace artificial intelligence (AI) and machine learning (ML) to optimize your use of data, then you’ll be left behind.
Today, data scientists are under more pressure than ever to make sense of complex, unstructured, or incomplete consumer data sets, while marketers struggle to draw conclusions from ever-changing consumer wants, needs and behavior. Let’s examine the forces that brought us here and the types of data enrichment and analysis that will be necessary to tackle these challenges in the next decade.
The 2010s: The Age of Big Data
Consider the last ten years as the age of big data. Data collection is more than widespread; it shapes the way that the world’s best companies do business. When compared to non-data-driven companies, data-driven organizations are:
- 23x more likely to acquire customers
- 6x as likely to retain customers
- 19x more likely to be profitable.1
What does it mean to be data-driven? With the sheer volume of data available, traditional measurement, reports and projections fall short of what’s possible. For meaningful insights and customer analytics, it’s necessary to leverage AI and ML to transform that unstructured information into data that’s clean, comprehensive, and actionable. AI is already well on its way to pervasiveness:
- By 2021, 80% of emerging technologies will have AI foundations.2
- Worldwide spending on artificial intelligence systems is forecasted to reach $35.8 billion in 2019, a 44% increase over 2018.3
- Demand for AI talent has doubled in the last two years. And despite increased talent in the workforce, there are two open roles available for every AI professional working today.4
The 2020s: The Brink of a Data Explosion
If you think data is big now, we’re nowhere near what’s coming. From phones to smart homes, to computers and cars, we are more connected than ever. And nearly everything we do leaves a digital trace. Google, Facebook, Amazon – all of these tools see what we search, like, share, buy, watch and listen to.
This connected environment complicates the customer journey. 15 years ago, the average consumer typically had two touchpoints with a company before buying an item. Today’s consumers have an average of six touchpoints (and often more) before making a purchase, with many of these touchpoints in channels outside of the company’s control.5
Furthermore, disruptors continue to turn customer engagement on its head. Think of how the money-exchanging app Venmo has supplanted banks, how Peloton brought high-end cycling studios into users’ homes and how direct-to-consumer brands have changed the way we shop for things as mundane as socks and razors. New ways of doing business require radical new methods of data collection, analysis and prediction. Disruptor data can be a source of never-before-seen customer insights, but only when used with the right tools and data sets.
How to Prepare for the Age of AI and Machine Learning
It’s been said that “data is the new oil.” That’s not just a nod to the mad dash toward bigger and better sources of data. The real truth in the metaphor lies in the fact that data, like oil, isn’t usable until it’s refined.
AI and ML are the primary tools for refining data into usable insights. While this has become common knowledge, many organizations still face hurdles to more widespread adoption of the technology within their organization, specifically:
1. Skills of staff: 56% of companies lack the human resources to use AI/ML.
Finding people with the right skills to manage this kind of technology has become a real challenge. The people who do have the right skillset can be few and in very high demand. The good news is there are resources you can tap into through university programs and service providers that can fill that gap for you.
2. Understanding the benefits and uses: 42% of companies have fear or uncertainty surrounding AI solutions.
The list of use cases that AI and ML can support is long, but some of the most common applications we see at Mobilewalla include:
- Better understanding of customer behavior to drive retention and acquisition
- Improving customer engagement through a more personalized experience
- Understanding competitive preferences and activity
As a starting point, make a list of the biggest challenges your business is trying to solve and work from there. No doubt there is a way for AI and ML to better inform strategies to address those challenges, if not provide a solution.
3. Data scope or quality: 34% said data insufficiencies prevent Al/ML adoption.6
Data insufficiency is an extremely common problem that can be easily addressed by enhancing your first party data with third party data bought through a consumer data provider like Mobilewalla.
While first party data is considered extremely high-quality, it generally lacks scale, which limits your ability to deliver insights that drive strategic decision-making and overall business results. Third party data gives you the broadest view of consumer demographics and behavior and is critical input for AI models and effective audience targeting.
As we enter into the next decade, turning data into insights using AI/ML will be critical to the success of your marketing efforts. Don’t be overwhelmed by common barriers to entry – clean, comprehensive data may be closer (and more crucial) than you think.
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