Data science – it’s the talk of the town for certain. And data scientists – those who develop algorithms to mine huge amounts of data, analyse it, categorise it and use it to inform business decisions – are now in high demand.
That demand often dictates significant budgets for headhunting the best professionals, developing custom software or signing a contract with a technology vendor who can provide you with machine learning algorithms. For SMBs, investing in machine learning may seem insuperable at first. The good news is that now there are AI and machine learning tools in place that will not break the budget and that could do a lot of the heavy lifting when it comes to business decisions.
So, do you need to invest in data science as well? Here are five signs indicating that it may be time to do so:
Traditional marketing has relied on attempting to define a customer base, then conducting manual research to locate that base, and then creating marketing campaigns that try to reach them. This takes time and is very staff-heavy.
Investing in a data science platform can remove some of that heavy staff cost while providing insights based upon real data. This moves marketing decisions into science, rather than gut feelings.
According to a new study from Forrester Research, 80% of companies who have already adopted a data science platform report a revenue growth exceeding 5%. Nearly half of respondents noted that the company’s growth, as well as the bottom-line profits, are exceeding shareholders’ expectations. Additionally, 64% report that they are now leading in their respective markets and have a higher share than any other competitor.
The need for relationship-building with current and potential customers is crucial for business. But the problem is that there’s no “one size fits all” approach to that. This is because your customers fall into different sub-categories depending on where they are in your sales funnel. Delivering the content and action that will move those prospects further down the funnel is the key to increasing your conversion rates.
A 2017 study by Monetate resulted in some compelling results. 79% of marketers surveyed stated that they exceeded their ROI goals, and they had a personalisation strategy that they documented, monitored, and analysed. And 95% of those who had a documented personalisation strategy realised increased ROI of 3X the revenue before they had such a strategy.
Accurate and timely lead scoring is a common problem. And the causes are many. One issue is that the data you have about your prospects is siloed deep within your CRM and it takes just too long to operationalise it.
Lead scoring has long been a function of marketing and sales departments. They attempt to categorise leads as hot, warm, or cold. The problem is that this has been done manually and is often based upon subjective rather than objective information. Enter big data and algorithms that provide predictive analysis based upon huge sets of data – data that can help scientifically identify the elements of good leads. In a recent McKinsey report, companies have realised as much as 30% greater conversion rates when using lead scoring algorithms.
Deciding on the right metrics and channels to measure can be tricky if you are unsure of your current goals and/or most successful activities. And you are not alone in this struggle as 44% of businesses report that they cannot precisely measure social media marketing ROI and quantify the results.
Data science can help you estimate the specific return on each of your marketing strategies from all of your audience categories and pinpoint the numbers from each of those campaigns. When you know which campaigns are bringing in the most revenue, you will know what to keep, what to modify, and what to dump.
Case in point: Red Roof Inn has developed an algorithm that sources weather severity, travel conditions and flight cancellation information, and organises it by locale. It uses this data to deliver targeted mobile ads to stranded passengers who can then easily book a room. This targeted marketing campaign has resulted in a 10% annual increase in revenue.
Staff costs are usually the biggest budget line item for a company. It’s expensive to recruit, hire and train new teams, not to mention the longer-term costs of salaries and benefits. When a business can cut personnel costs by using data science to streamline and increase efficiency, it’s a win-win.
Here are just a few ways in which data science can bring greater efficiency:
Investing in data science is a must for companies that want to maintain a competitive edge and grow their revenue. It can provide objective data, based on scientific algorithms, that will take the guesswork out of who your customers are, where they are, and what they want. It will allow you to streamline processes and make decisions based upon objective information rather than gut feelings. In the end, data science puts you out in front of your competition.
Then check out these other articles to bring yourself up to speed:
The marketer’s three-minute guide to data science
Expert interview: The importance of data science for today’s marketer
Three great examples of data science in marketing
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Categories: Data & Analytics, Data Science
Categories: Data Science