A Data Science Twist on SWOT Analysis for Small Companies

$50 billion before 2020, can help even solopreneurs compete with industry leaders. The beauty of Big Data is that through online, scalable and affordable tools you can have the luxury of a dedicated marketing, HR or finance team.

The real question is, do you need these tools for your company? As long as you have questions like: “Which are our top performing products? Who is buying them? Which price is best for each segment? Who are our direct competitors and what are they doing better? What will be in high demand next year?” you too can benefit from the power of Big Data.

SWOT Analysis with Big Data

One of the fundamental business strategy tools is the SWOT analysis template. This gives companies the chance to look at what makes them unique and where they could improve. It is also a tools to assess the influence of technological changes which could translate into growth and identify those items that could hinge profit, including competitors.

Looking into the right combination of data, taking both internal factors (sales, cash-flows) and external influences (industry metrics and benchmarks), with the help of Big Data analysis, you can get real-time answers to questions such as those above and more. When using external data, be sure to choose only reputable sources, such as international organizations or official census records. Let’s see how data science can make a change in each step of the SWOT process.

Strengths - Validate decisions

Data science lets you dream and play with different scenarios, answering those “what if?” questions that are at the core of business development. Using analysis tools, you can variate parameters such as price, volume and change conditions to meet different expectations (pessimistic/optimistic) and take the right course of action after careful analysis. Make the most out of your strengths like a low selling price, a unique technology or a patent.

Weaknesses - Risk analysis

The risks for a small company are limited compared to large enterprises, but their effects can be much more intense, often leading even to bankruptcy in the first five years. Big Data can help you create models and analyze patterns that show what you should avoid or limit. For example, you could learn that promotions are hurting your profits or that a particular group of consumers have an ROI margin that is far too low and would better be abandoned.

Opportunities - Analyze trends

Big Data analysis is perfectly suitable for unveiling patterns in sets of data. These are the base for identifying trends and opportunities. For example, if you see on Google Trends that people in your area/country are looking for a particular item and you sell similar ones, it could be a good idea to update your stocks and increase your marketing efforts to seize the rising interest. Even social media channels like Twitter have a “trends” feature that can be useful and is free.

Threats - Risk management

Use data to foresee possible cash-flow problems. Take into account that customers seldom change their past payment behavior and include that into your future models. Another serious problem, as highlighted by InData Labs, is predicting customer churn patterns. Data science can help you uncover behavioral patterns, including asking for a negotiation or failing to pay, that occur before a customer decides to exit a contract. The same logic applies to employees that want to leave the company.

Also, use tools to look at what your competitors are doing. There are a few options that show you what keywords they are using for their pages, where their backlinks come from and more. You can use this as a sneak peek into their marketing strategy and try to replicate the best choices.

Possible challenges and solutions

While enterprises have embraced the benefits of Big Data adoption, small businesses are still afraid of the potential drawbacks. Costs are the primary reason for concern. While a few years ago this would have been a valid point, current cloud-based solutions offer pay-as-you-go models that adapt to the needs of each organization. In fact, it could even mean an increase in efficiency by minimizing human errors. If you are afraid of wasting money and not seeing consistent ROI changes, you can start using free tools like Google Analytics first and then move towards paid solutions for specific problems.

Another fear is related to the learning curve. Again, this is not something to be worried about. Most data science systems have user-friendly interfaces that are not more complicated than regular business tools such as Microsoft Office. In fact, the best come with visual dashboards that make the analysis process easy to understand at a glance.

Best tools for small businesses

To get acquainted with the data science world, you can start by exploring the Google Analytics suite after connecting it to your website. You can find out about trends, what people are looking for, and look deeper into your visitor’s profiles. It is possible to create audiences, evaluate their interaction and even do marketing campaigns. Also, if you use social media pages, these come with their own monitoring tools.

If you are looking into a centralized solution to gather all the data you intend to use under a single umbrella, IBM’s Watson may be a choice. The great thing about this tool is that it uses natural language processing. Depending on your goals there are numerous platforms available, including Kissmetrics for web analytics, InsightSquared to connect your existing CRM tools and more. 

Final thoughts

Dismissing the opportunities brought by Big Data at this point is similar to rejecting computers in the 90’s and keeping your business exclusively on paper. Data science has crossed the chasm towards mainstream adoption. While it currently offers a competitive advantage to companies using it, soon it will make a difference between survival and oblivion. It’s time to use “small” as a synonym for agile, not fragile.

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