Product Development Analytics.

Analytics is crucial to product management for one significant purpose: product improvement. Without analytics, product development would be reduced to a series of shots in the dark and product teams would be clueless as to how well their products were meeting user expectations.

The measurements taken by metrics and the insights provided by analytics enable product teams to make informed decisions about upgrading product functionality or adding capabilities. Without measuring and analysing the results, they would have no idea if the revisions implemented are effective or even necessary. They would be flying blind.

The five key roles of analytics in product development and management are:

Product viability. A variety of analytics tools can verify product concepts, helping developers test, learn, adjust and retest to speed up the product design and launch process.

Informed product decision-making: Analytics has made decision-making more objective, reliable and faster. While intuition based on experience and expertise can still play a valuable role in product development, it can and should take a backseat to objective analytics.

Product progress measurement. Product analytics can inform team members about which features are working and which are not. Analytics plays a critical role in creating an accurate product roadmap that can inform a firm of where their product is currently, where it wants it to be and how to get it there.

User experience insights: Product teams can use analytics to understand why users are buying their product and how they are using it.

Product development inspiration-. Analytics can jump-start innovation and help an existing product remain viable for an extended period of time. Quantitative analytics, used in conjunction with qualitative techniques, can provide a more holistic view of a product to help product management teams make the kind of focused improvements and adjustments that will help maintain that products value and improve its longevity.

Over the past decade or so, data generated by metrics and the analytical tools used to tease insights out of it have transformed product development and management. While some product team members may lament the demise of‘seat-of-the-pants’ engineering, the reality today is that without data and the analytics to understand it, effective product development and successful product management are simply not possible.

Predictive Analytics in Product Development

Business analytics revolves around three domains, namely descriptive analytics, prescriptive analytics and predictive analytics. Descriptive analytics is the use of data to understand past and current business performance and make informed decisions. Prescriptive analytics, on the other hand, focuses on identifying the best alternatives to minimize or maximize some objectives. Predictive analytics predicts the future by examining historical data, detecting patterns or relationships in these data and then extrapolating these relationships forward in time.

Organizations have long relied on traditional product development tools and approaches, including FMEA, CAD simulations, design of experiments and value stream analysis, to heighten efficiencies, eliminate waste and optimize costs. However, given the ever-increasing volumes of data that flow into and through companies, conventional product development technologies and tactics are no longer sufficient. Although product developers continually look for better ways to handle the abundance of data at their disposal, most don’t have the right tools to manage it, make sense of it or apply the insight it provides to support future product initiatives. Innovative companies know that data-driven insights and decisions can help improve all aspects of product development. According to McKinsey s global survey, many are already applying big data/analytics to:

Analytics resources for start-ups (https://segment.com/catalog/ #integrations/analytics.)

Figure 8.3 Analytics resources for start-ups (https://segment.com/catalog/ #integrations/analytics.)

■ Improve research and development (R&D)

■ Develop new product strategies

■ Identify new market segments

■ Deepen customer knowledge/relationships

■ Improve customer segmentation and targeting.

Predictive analytics applies across the product development value chain as canvased byjoshi and Kansupada (2020) and depicted in the Figure 8.3 below.

Entrepreneurship Analytics

Since 1930s, entrepreneurship has been identified as a positive driving force for regional economic growth and development (Schumpeter, 1934). The progress of many countries is largely attributable to entrepreneurial activities and entrepreneurship-driven policies. To combat the growing unemployment rate in most emerging economies, attention has drastically shifted to youth entrepreneur- ship as a means of gainful employment for the teeming youth population of those countries. While entrepreneurship may be a sure bet for employment and growth, it is no doubt a herculean task to start and successfully manage a business through its thick and thin. Thanks to the advent of analytics that has transformed virtually all spheres of human endeavours, including entrepreneurship.

In business, analytics can be described as the use of data, information technology, statistical analysis, quantitative methods and mathematical or computer-based models. Data analytics techniques enable firms to use effectively such abundant data, giving SMEs a competitive edge and increasing their productivity such as reducing costs, enhancing marketing practices and strengthening their ability to identify or foresee trends (Bianchini & Michalkova, 2019).

However, SMEs and entrepreneurs face difficult challenges in accessing and analysing relevant data. On the one hand, limited digital skills by management and employees may fuel misperceptions on the actual risks and benefits deriving from adoption of recent digital technologies. On the other hand, small businesses typically find it more difficult to identify, attract and retain the specialists needed to deploy effective data analytics. In addition, SMEs also face limited financing options and burdensome regulatory requirements (for example personal data protection) often represent additional barriers for SMEs. Governments of nation are increasingly acknowledging the importance of data analytics for SMEs and are taking action to address these key challenges. Measures include offering training and skills development programmes to entrepreneurs and SMEs’ employees; introducing regulatory reforms that contribute to enhanced data management practices by SMEs; promoting data sharing and diffusion; supporting knowledge exchange among SMEs, as well as public institutions, business associations and other stakeholders; and providing financial support for data analytics projects in SMEs.

Analytics for Start-up Entrepreneurs

One major source of trepidation for start-up entrepreneurs is how to navigate the vast data analytics ecosystem to identify what is suitable and convenient for their chosen business. The increasing number of new data analytics tools and proliferation of new terminologies further compound this fear. Data analytics is not as complex as some data scientists portray it. Most start-ups are well inadvertently involved in data analytics without necessarily deploying the latest technologies and tools. However, there are a vast array of analytics resources available to start-ups to leverage on as shown below (Figure 8.4).

The stage, nature and complexity of a business determine largely the sophistication of analytics tools required. It is, however, worthy of note that getting valuable and actionable data requires some more effort as this can be quite challenging especially for new entrants into the industry. Here are a few guidelines for start-ups desiring to maximize the benefits of analytics in their business.

Predictive analytics in product development value chain (Adapted from Joshi and Kansupada (2020).)

Figure 8.4 Predictive analytics in product development value chain (Adapted from Joshi and Kansupada (2020).)

Choose the Right Analytics Team

Data analytics can be complicated if the right team is not assembled. What was once the domain of Excel spreadsheet enthusiasts has now become a specialized skill for data analysts and scientists. While Excel still has its place, entrepreneurs will be able to get a lot more out of data if they hire a team with more advanced skills. A good data analytics team will need spreadsheet skills, but analysts should also have data analytics programming skills in Python, R or SQL, as well as a good grasp of statistics. They need to know what is realistic for start-up. A data scientist with a doctorate and ten years of experience in deep learning for autonomous driving might look great on paper, but he may not have the level of industry experience needed by a start-up. Keep high, but reasonable, expectations when hiring data analysts, and look for talented individuals with room to grow as your start-up grows.

Collect the Right Data

Data is the foundation of your data analytics. Even the best analysts in the world will not be able to do much for if they do not have good data to work with. Start-up must be sure they have the data they need for accurate and meaningful analysis. If they are in doubt of what they need, they should consult their analysts. If they have the right team, they will have a clear idea of what is needed and what is not. For example, a start-up in ecommerce will almost certainly have Google Analytics or some other analytics tool set up already. However, they may also want an extension or package to handle A/В testing to see how different page layouts or copy affects user experience. As an ecommerce company, would you need a heatmap to see how click patterns work?

Probably not. However, a start-up that is building a mobile game would love this type of insight, and could use it to improve the product by making specific interface choices informed by player actions. What a start-up will need depends on the specifics of the business. Deciding what data to collect is an aspect he should research into and implement as early as possible, because the better the data quality, the more effective the analysts can be in their analysis and the better the intended output.

Make Key Technology Decisions Early

Similarly, it is important that start-ups choose their tech stack early. A company that is built on a faulty foundation is not likely to thrive, and constantly switching out the solutions in its stack will wreak havoc with your data analytics. The choices made by start-ups, including fundamental technological ones like databases, will affect the types of analyses they can perform. A poor choice of infrastructure can be debilitating. NoSQL databases like MongoDB have become popular in recent years as they allow for quick scaling and building of product. However, this comes at the cost of being able to perform joins across data types. Traditional SQL databases like MySQL and PostgreSQL are much better at this. While it may not seem like start-ups need those features at their infancy, they must keep in mind that any changes to their system once it is up and running are likely to be disruptive and expensive. Therefore, it is better to start out with a tech stack and a database solution they can grow into, which will not limit the types of analyses their team can perform.

Measure Your Results

Start-ups should establish key performance indicators for their data analytics team. They must measure the return on investment in data analytics based on established criteria. For instance, Facebook has an analytics team which measures how relevant a post is to a user and its success is tied to this measurement. Being able to measure data team’s effectiveness is key. However, moderation is key to avoid going overboard. A common mistake is having too many metrics. The data analytics team must then balance multiple measurements, and it will be difficult to narrow focus. Like any other team, the data team needs a clear direction, so pick one or two KPIs and stick with them.

Find the Supportive Investors

The right investors can be the make-or-break factor in data analytics for a start-up. Some investors may scoff at the idea of a team solely devoted to data analytics, thinking it is only needed at a larger scale. Other investors will demand an experienced analyst as an early hire before they experience data surge. Ideally, entrepreneurs want to find investors who understand that a start-up should have a data analytics team when data is available to assess. The older the data gets, the less useful insight it can provide, so once an entrepreneur is at the point of generating and collecting data, it makes sense to bring in an analyst or analytics team to help monetize it.

Growth Hacking for Start-ups

One of the biggest uses for data analytics at a start-up is growth hacking. Often, it is valuable to know what kinds of things correlate with users signing up for your site, or making a purchase so that one can double-down on strategies that prompt users to do those things. For example, early on in its development, file hosting provider Dropbox analysed its data and concluded that users who shared a file on their platform were more likely to be repeat users. They never figured out exactly why because all that mattered was the result. Dropbox rearranged its website to make sharing more accessible, and added hints to prompt users to share. Their user count skyrocketed because of the changes to the platform. It was analysing the data and discovering the connection between shares and user signups that made Dropbox’s growth spurt possible. There is the likelihood that entrepreneurs will want the data team to be in search of similar insights at their business. Once they are able to systematically identify the actions that lead to the customer growth, their business will surely gain competitive edge and this will invariably lead to a growth in revenue.

Conclusion

In this chapter, an attempt was made at conceptualizing NPD in contemporary times. From the preceding sections, it was observed that the newness of a new product is multidimensional. Therefore, it behoves on the firm to identity the strategy that best fits its intended objective of birthing a new product. The increasing level of technological advancement has opened more vistas to seamlessly achieve this goal. Increasingly, product developers now rely heavily on analytics to improve every stage of NPD. This chapter also discusses the importance of entrepreneurship analytics for young entrepreneurs and highlights how start-ups can maximize the huge potential of analytics to advance their business.

References

Bianchini, M. & Michalkova, V. (2019). Data analytics in SMEs: trends and policies. Working Paper 15■ OECD SME and Entrepreneurship, https://dx.doi. org/10.1787/lde6c6a7-en.

Chen, H.H., Kang, H.-Y., Lee, A.H.I., Xing, X., & Tong, Y. (2007). Developing new products with knowledge management methods and process development management in a network, Computers in Industry, vol. 59, no. 2-3, 242—253.

Cooper, R.G. (2019). The drivers of success in new-product development, Industrial Marketing Management, vol. 76, 36-47.

Corrocher, N. & Zirulia, L. (2010). Demand and innovation in services: the case of mobile communications, Research Policy, vol. 39, no. 7, 945—955.

Dataquest. (2020). Data Analytics for Startups: What You Need to Know. Retrieved from: https://www.dataquest.io/blog/data-analytics-startups-what-you-need-to-know/.

Griffin, A. (1997). PDMA research on new product development practices: updating trends and benchmarking best practices, Journal of Product Innovation Management, vol. 14, 429-458.

Johne, F.A. & Snelson, P.A. (1988). The role of marketing specialists in product development, Proceedings of the 21st Annual Conference of the Marketing Education Group, Huddersfield, vol. 3, 176-191.

Joshi, A. & Kansupada, H. (2020). Predictive Analytics: Accelerating & Enriching Product Development. Cognizant, pp. 1-8.

Loch, C.H. & Kavadias, S. (2008). Managing new product development: An evolutionary framework. In C.H. Loch & S. Kavadias (Eds.). Handbook of New Product Development Management, (pp. 1-666). Oxford, UK: Elsevier.

Schumpeter, J. A. (1934). The Theory of Economic Development: An Inquiry into Profits, Capital, Credit, Interest, and the Business Cycle. New Brunswick, NJ: Transaction Publishers.

Trott, P. (2017). Innovation Management and New Product Development. Edinburgh, UK: Pearson Education.

 
Source
< Prev   CONTENTS   Source   Next >