During my time at Radius, I’ve had the opportunity to talk to hundreds of companies that are evaluating predictive, ranging from early adopters looking to unlock revenue growth to marketers curious about this ‘hot’, new emerging technology.
Throughout these conversations, there has been one common question that I’ve heard time and time again – what other options do I have available besides predictive analytics?
So, to help businesses that want to explore alternatives to predictive, or for the skeptics that don’t see the value in it (you know who you are), I’ve compiled this list of alternatives and highlighted why they fall short in helping you become more successful with your go-to-market efforts.
What Are The Alternatives To Predictive Analytics
There are primarily three alternatives to predictive analytics:
- Do nothing – Maintain the status quo and go about business as usual
- DIY – Hire an internal team of data scientists to build your own system
- Partner – Contract an outside firm to build and manage a system for you
1) Do nothing – Maintain the status quo
Your first alternative to predictive could be to just leave things as is – you could not fix the problems your organization is facing and continue with the same challenges.
Let’s say you decide to go this route for another six months – what would be the impact of that choice?
Using a combination of top- and bottom-line levers for measuring predictive ROI, calculate how much revenue you would be missing out over the course of those six months. (If you’re not familiar with which levers to measure for predictive ROI, subscribe to our Predictive Evaluation series)
Each time you run a campaign and don’t leverage a heightened level of marketing intelligence, you’re not maximizing your revenue. Or, worse yet, you’re making it harder to hit your targets and missing out on potential revenue that could be attainable with predictive.
Let’s look at an example
Imagine you’re running a webinar and want to reach the maximum number of target prospects. Relying on marketing automation data and targeting limits your ability to target the right prospects versus using predictive. Predictive also enables you to source new prospects for the campaign. Below is data from a real webinar campaign.
You can see how much lift predictive gave this campaign – both in reach and pipeline generation.
2) DIY – Hire a team of data scientists
The second option that you might be considering is to hire a team of data scientists and build your own system internally.
To understand the limitations of building a predictive solution internally, it’s first important to understand the foundation of predictive applications. This image from SiriusDecisions outlines the three primary building blocks for predictive – data, data science, and statistical modeling.
Source: Applying Predictive Analytics: What is it and how does it help?, SiriusDecisions
- Data – Availability of large amounts of data is a critical factor in being able to predict outcomes. Even before you jump into data science or modeling, you first need access to a large sample of data that is up-to-date, accurate, and readily available. While you can purchase data lists from providers, it’s incredibly expensive and difficult to keep up with the constant changes in data.
- Data science – Beyond the raw data itself, you also need to be able to accurately associate data sources across digital and non-digital touch points to the relevant leads and companies, which requires advanced data science expertise. Most organizations address this by hiring data scientists, but we’ll talk about why that’s easier said than done later in this post.
- Statistical modeling – Last, but not least, you need to build complex statistical models that also include machine-learning to continuously improve your predictive models. This is another area that will require hiring analysts with sufficient expertise who are capable of building such models.
Besides the challenges that prevent marketers from acquiring accurate, up-to-date data in large enough quantities to enable predictive modeling, there are three reasons why hiring data scientists internally is not cost effective or impactful:
Firstly, finding a data scientist is difficult. There is simply a deficiency of data science talent in the market today.
Second, even if you do find a data scientist or two, they are extremely expensive to hire with an average salary of $127k. Third, and most importantly, data scientists need a high-level understanding of complex data science principles, not to mention a mastery of data, BI expertise, and a platform that enables them to build models in the first place.
When you combine all these factors, it’s far cheaper and more effective to simply leverage a predictive analytics solution that is readily available and addresses all of your business needs.
3) Partner – Outsource to an external firm
The third alternative is to partner with an outside firm that will help build and manage a system for you.
In most cases, the firm you’re partnering with faces many of the same challenges mentioned earlier with the DIY approach. The firm is likely to be struggling to put together data from disparate sources and using expensive tools to rebuild solutions that already exist. But even beyond these shortcomings, there’s the major challenge of communicating and implementing predictive models in the context of your marketing workflows.
For example, let’s say your firm of choice delivers sophisticated models built on great foundational data. What if they’re delivering CSV files to you? You still need to clean up the data and input it manually into your marketing system in order to launch your programs. This leads to a disconnected workflow and an inefficient process for leveraging predictive insights.
With a lack of suitable alternatives to predictive, marketers are only left with one viable option – adopting a SaaS predictive solution.
Making The Case For A Predictive SaaS Platform
If you examine the traditional decision-making system, you’ll find that it can be segmented into three phases – first of which is research and development to build systems.
Many teams look to outside professionals to build systems that help them address their specific needs and challenges. After all, creating new systems is necessary for addressing new problems. But building a system that addresses common challenges has now been the long-term agent for cloud-based SaaS, which is why consultative partnerships or building internal resources are many times inefficient. The challenges presented by data, modeling and integrated workflows that allow for effective campaigns and customer acquisition have become ubiquitous, allowing for a software solution like predictive analytics to address the problem at scale. We’ve seen this evolution with previous platforms, and over time, it has created a marketing stack that we know today.
The decision to buy, build, or partner is common across any company. It’s important to look at the specifics around why buying predictive, versus hiring internally or outsourcing, will allow you to implement the best data-driven, scientific approach faster, easier, and for a fraction of the cost.
Need help selling predictive to internal stakeholders, assessing vendors, and building an ironclad business case? Learn how by subscribing to our Predictive Evaluation series.