Red tapism, corporate innovation and growth

Innovate or die – haven’t we all heard this? Add red tapism and innovation in the same sentence, sounds conflicting, right?

Organisations try to live up to “Innovate or die” by trying to become Agile or migrating to cloud services or adopting OKRs, basically moving in the right direction to be able to deliver better and faster. As technology advances exponentially, customers become more demaning, organisations must now be able to quickly respond to market demands in order to compete, which requires agility.

However, trying to be agile in an established, organization with legacy systems and non-agile methods is a big challenge. Untangling teams and systems that have always worked in silos is a big hurdle and the hardest part of any organisational transformation, not to forget the red tape created by outdated processes. Earlier, organisations created processes and procedures to ensure predictable outcomes, to mitigate risks. The processes designed did not have much room for experimentation or agility. But in the current digital landscape, this type of bureaucracy is simply too time consuming and not at all cost-effective.

• Most enterprises have standardised tedious approval processes where some of the people approving do not even possess the technical know how to judge or review the matter in question. This ends up in unending rounds of justifying the simplest decision.
• Procurement teams also add to the red tapism making it difficult for teams to acquire services and products that can speed up their development. We have all been through this – waiting months to get Slack or JIRA approved. And IT Security will not allow Trello or Google Drive, so go figure!
• And then there is the fear of cloud solutions. There is no denying privacy is a major concern when it comes to data and customers want to ensure that the services and products they use, handle their data well. But a cloud solution provider is more likely to have robust, well-configured firewalls and data security practices than an average enterprise, as it is the focus of their business. Keeping in mind that the cost of regulatory compliance will be substantial, but the cost of non-compliance will be higher, is important while choosing cloud service vendors.
• To top it all there is the fear of unknown, which is a huge blocker for innovation, it is therefore important to educate and get a buy-in from everyone involved on a transformation journey.

To be able to innovate, enterprises need to deliver end-to-end business value in increments, test and validate results before starting full scale development. Creating a culture of testing and experimentation demands processes and methodologies that support faster delivery of customer-centric value, with constant room for improvement.

Starting off testing a few assumptions that could lead to a potential minimum viable product should not require written approval from legal, compliance, finance, risk management, procurement, etc. Experimenation could be conducted with data masking and that should not entail long winded paperwork such as a detailed risk analysis and architectural artefacts.
Red tapism is a sure shot way to kill creativity which in turn ensures no innovation or improvement.

If organisations really want faster qualitative deliveries, freethinking leaders should not be afraid to rock the corporate boat and cut some slack in terms of obsolete processes and procedures.

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Managing corporate innovation

The time is ripe for corporates to embark on a journey of innovation. Having said that it is a rocky road for enterprises that have been in existence for half a century or more, having made hay when the sun shined and thus gathering a good amount of legacy systems and processes on the way. Some organisations that have have chosen to invest in innovation prefer to run the innovation labs separately, far from the core business, the reason being that innovation should not get bogged down by corporate beauracracy. A big part of innovation is experimentation — experimentation with thoughts and ideas and prototyping. Business leaders have a herculean task of ensuring innovation for the businesses of the future while continuously improving the incumbent corporate machinery that generates the revenue necessary for future investments.

Innovation is about inventing new products or services that solve customer needs and can be monetised. Innovation involves trial and error and learning from the same, a structured method of experimentation leads to better tracking of ROI. The pentathlon framework of innovation articulates the methodology from ideation to market launch. The influx of ideas, which form the innovation backlog, can be either disruptive ideas or new ways of resolving existing business challenges.

Each part of the innovation funnel has to imbibe a fail fastand and an iterative approach for further ideation with feedback loops. Every new invention undergoes inception, improvements and adoption followed by stability and subsequent depreciation. If there are many trains of thought in the innovation projects pipeline, there has to be an order in the way of prioritizing the projects. Prioritization of innovation projects should depend on

• Marketability — is there a potential market for the product/service?
• Feasibility — is it possible to deliver the project with the resources the company can afford?
• ROI — How soon is the breakeven point?
• Time to market — how soon can the MVP be launched?

The implementation of innovation projects are not very different from other corporate projects, involving a portfolio selection based on the prioritization and business urgency. The next stage involves scoping and development, preceeded by prototyping before scaling. Post validation and launch, the impact has to be measured and analyzed to understand the product adoption and customer experience. The insights from measuring innovation efforts lead to newer ideas or incremental improvements to exisiting business processes and/or the innovative product under consideration. There has to be a relentless flow of insights into the innovation funnel to finetune the ideas being considered for prioritization, implementation and launch.

The very existence of an innovation foundry within a coporate house has to be justified to the investors and shareholders. As a means to create a process of accountability there should be KPIs defined, some examples being:

• Number of ideas considered for prioritization
• Number of ideas that were productionalized
• Number ideas that have lead to business process improvement
• Value added (Value = Accrued benefits — costs)
• Time to breakeven

The major factors that influence an innovative culture at a corporate level depend on the company culture – the ability to thrive during change and adaptability to new market conditions. It is of utmost importance to recognise and reward the people who contribute to innovation and showcase and communicate the succesful results. Innovation labs within corporates cannot be run separately forever, the outputs from the innovation exercises need to flow back into the day to day business and the challenges from the core business need to be worked upon in the innovation labs.

With innovation being a crucial area of focus for most organisations, there are a lot of ideas floating regarding innovation management. It can be rather chaotic with buzzwords like Big data, AI, Robotics, Augmented Reality etc. being thrown around callously. Technology is a great enabler for business strategy, but great ideas arise from understanding customer requirements and the lack of products or services that fulfill the same.The most significant point of focus in any corporate innovation should be customer centricity and business gain.

Four steps to becoming a Data-Driven organisation

Not a day goes by when our LinkedIn news feed is not flooded with the mentions of AI and Machine Learning benefitting and changing the ways of mankind, like never before. This hype surrounding AI, Machine learning has resulted in most organisations jumping on the bandwagon without proper evaluation. A couple of years ago, the term Big Data enjoyed a similar hyped status but it has been losing it’s lustre to all the talk about AI and Machine Learning, lately.

The truth, however, is that, AI and Big data need to coexist and converge. Merely collecting and storing data in huge amounts will prove futile, unless AI and Analytics are used to generate meaningful insights that help businesses, enhance customer experience or increase revenue influx.

Making an organisation Data-Driven will take time and will happen in stages. While there are no sure shot ways to create a Data-Driven organisation, below are some ways that could lead to a change:

1. Strategy – It all starts with a clearly defined strategy in place, stating the Whys, Hows, Whos and Whens. A clear strategy helps in raising awareness across the organisation, about the topic in focus (data in this case) and creates a sense of urgency around the change process. It is imperative that the entire organisation understands the importance and implications of a data-driven organisation, thus encouraging people to update their skill sets and raise their level of data awareness. An all round data strategy should not only include the technology required for execution but the kind of competence and people skills and the sort of conducive atmosphere required for a data-driven organisation to thrive.
2. People – Just as there are different kinds of skills required within a Marketing or a Software organisation, there are different skill sets for the different job roles within a data organisation. But due to the hype surrounding Machine Learning and AI while companies lack the practical knowledge in data know-how, the tendency is to either hire the wrong people or assign the wrong tasks to the right people! Not everyone has to be a data scientist in the data organisation. There will be people required to work on data architecture, data infrastructure, data engineering, data science and the Business Analysts. These could very well be the same person, if the organisation is lucky enough. But it is unfair to hire a data engineer and assign him/her the task of building Predictive models or hiring a data Scientist to be told to develop BI reports. Strategists will have to spend the time required to understand the nuances of skills and expertise required in a data organisation but it will be worth it, to retain and grown the talent pool required for a Data-driven organisation.
3.  Patience – Creating a Data-driven organisation will require ample amounts of patience and perseverance. If data has not been involved in the decision making process, earlier,  then the data is most probably not in a state that can be used readily or maybe there is no or not enough data to begin with! In that case, it has to start with gathering the data required to achieve the business goals. Transaction systems have a very different database design than the data storage mechanisms used for Analytics purposes, which entails a design and architecting process before being able to analyse the data. Moreover, as Analysts dig into the transaction data, they surely will encounter non-existence of relevant data, data retrieval issues and unearth data quality issues and data integration problems due to the existence of data silos. In a data-driven organisation, all data sources are integrated to provide a single enterprise version of truth, irrespective of Customer data or Sales or Marketing data. A data platform, integrating all business data sources, ensuring quality and data integrity and security is a time-consuming process. Organisations will have to take this lead time into consideration when strategizing a Data-driven decision making approach.
4. Organisational Culture – The purpose of a Data-driven organisation is to empower employees by means of data and information sharing to enable the organisation to collectively achieve the business goals. This approach requires employees to be data aware and not use gut feelings to make decisions and this could be a whole new approach for many. This new way of working requires organisational change management, educating people to use facts and figures to arrive at conclusions and make decisions. If an organisation is fairly data aware, in the sense that metrics are used to measure certain processes, in order to turn Data-driven , the organisation has to take steps to use data proactively (read Predictive Analytics) and not just summarise events that happened. The CDOs/ CMOs need to drive data awareness by showcasing quick wins and success cases of Data-driven approaches, as a means to use data as the foundation in every decision making process.

Some organisations may take longer to implement a Data-driven culture than others but there is no way an organisation can become Data-driven, just like that, one fine day! If the CDOs can gauge that the organisation has a longer incubation period then it is good to start with raising data awareness and introducing a BI/ Datawarehousing team. It is not recommended to directly leap on to AI, hiring data scientists, to be then left in a lurch if the organisation and the infrastructure are pretty rudimentary to handle their expertise.

A Data-driven organisation culture starts with the right strategy in place, followed by the right people and technology, evaluating and optimising the entire process, intermittently.

Continuous delivery of Analytics

 

I am biased towards Analytics not only because it is my bread and butter but also my passion. But seriously, Analytics is the most important factor that helps drive businesses forward by providing insights into sales, revenue generation means, operations, competitors and customer satisfaction.

Analytics being paramount to businesses, the placement of it is still a matter of dispute. The organisations that get it right and are using data to drive their businesses, understand fully well that Analytics is neither a part of IT nor a part of business. It is somewhere in between, an entity in itself.

The insights generated from Analytics is all about business drivers:

• Performance of the product (Product Analytics)
• How well is the product perceived by customers (Customer Experience)
• Can the business generate larger margins without increasing the price of the product (Cost Optimisation)
• What is the bounce rate and what causes bounce (Funnel Analytics)
• Getting to know the target audience better (Customer Analytics)

While the above insights are business related and require a deep understanding of the product, online marketing knowledge, data stickiness mastery and product management skills, there is a huge IT infrastructure behind the scenes to be able gather the data required and generate the insights.

To be able to generate the business insights required to drive online and offline traffic or increase sales, organisations need to understand their targeted customer base better. Understanding customer behaviour or product performance entails quite a number of technical tasks in the background:

• Logging events on the website or app such as registration, add to cart, add to wish list, proceed to payment etc. (Data Pipelines)
• Having in place a scalable data storage and fast computing infrastructure, which requires knowledge about the various layers of tech stack
• Utilising machine learning and AI to implement Predictive Analytics and recommendations
• Implementing data visualisation tools to distribute data easily throughout the organisation to facilitate data driven decision making and spread data literacy

As is the case, Analytics cannot be boxed into either Tech or Business. It is a conjoined effort of both business and tech to understand the business requirements and translate the same into technically implementable steps. Many organisations make the mistake of involving Analytics at the end stage of product or concept development, which is almost a sure shot fiasco. Analytics needs to be involved at every step of a product development or customer experience or UX design or data infrastructure to make sure that the events, the data points that lead to insights, are in place from the beginning.

Delivering Analytics solutions is a collaborative effort that involves DevOps, data engineers, UX designers, online marketeers, social media strategists, IT strategists, Business Analysts, IT/Data architects and data scientists. A close co-operation between tech and business leads to continuous delivery of smarter and faster automations, enhanced customer experience and business insights.

Build. Measure. Evaluate. Optimise. Reevaluate.

 

 

Growth Hacker’s Marketing

Marketing is being disrupted and no more run by only traditional non-technical marketeers. Marketing is supported by a wide range of – call it reporting, dashboarding, marketing analytics, marketing automation processes. Moreover, the startup scene is very exciting and a hot bed for innovation. Most startups spring into action sans a huge funding. The startups will have to grow exponentially, boasting a substantial customer base to be able to entice investors. Enter the growth hacker – with a single minded goal, growth!

Typically, the UX team designs the UX strategy, the product team develops the product, the coder codes in order to deliver the product and the marketeer tries selling the product. But with the new age disruptive marketing, the UX team, product team, code development team and the marketing team will have to work very closely, trying and testing every trick in the book to elevate growth. A growth hacker is a bit of all the above.

A growth hacker is more of a full stack employee armed with Swiss knife like multiple skill sets, analytical abilities being top rated. Growth hacking is primarily a focus within the startups, the budget being a constraint, lesser number of employees expected to contribute more. But with time, enterprise companies will adapt to growth hacking means of increasing revenue generation. Growth hacking is based on data, analyzing data to improve the business processes, to sell more, to convert more, to gain new customers and retain existing customers. Growth hacking does not entail data reporting only for the purpose of data visualization, it uses data to derive at hypotheses and reasoning to better understand and improve internet marketing.

So what’s growth hacking all about? Growth hacking is about

• Improving user experince by A/B testing to reduce bounce rate
• Content Marketing
• Designing, implementing and analyzing sales funnel to reduce drop rates
• Search Engine Optimization
• Channelizing all it takes to increase conversion rate
• Using analytics to track click stream data about consumer’s online behavior
• Analyzing past online or shopping behavior to be able to predict consumer’s probable behavior at the next visit
• Social Media marketing – paramount for startups on shoe string budgets. Using Facebook, Twitter APIs to analyze the demographics of consumers sharing and liking the products, consumer opinion in social media and competitor analysis
• Being able to analyze consumers that are likely to churn and the reasons behind, which can be addressed. Analyzing the response data from campaigns targeted at reducing churn, to measure campaign effectiveness.
• Improving omnichannel advertising and using analytics to analyze data to conclude the channel that yields most and finding potential market opportunities

From the above list, growth, data and analytics are evidently the point of convergence for growth hacking. Growth hackers have to be inherently curious, tenacious, analytical and above all innovative. Growth hacking is an an art, not just number crunching or coding. It is the ability to see beyond code, to be able to analyze the implications of new features or every change in any part of the business processes that drive growth.

As Sean Ellis says, a “growth hacker’s true compass is north.”