Advertising and marketing agencies are generally run by pretty smart people, right? So why are large parts of their operations still so dumb?
One of the major challenges agencies face is keeping up with the pace of technological change. They seem to be getting left behind.
It’s true that agencies employ relatively sophisticated algorithms to predict, for example, purchase intention, or detect negative social media chatter about a brand. But they have failed to apply the same sophistication to the management systems running their own businesses.
Change at warp-speed
A century ago, the life expectancy of a firm in the S&P 500 index was 67 years. Many household names, like Pan Am, Arthur Andersen, Woolworths, have simply ceased to exist; others merged or were gobbled up by bigger fish.
It’s estimated that a decade from now three-quarters of today’s S&P 500 will drop out of the index. A sobering prediction, and yet, making better predictions may just be the key to business longevity.
With machine-reengineering, processes evolve by a constant feedback loop, driven not just by accumulated data, but also by the predictive capabilities of machine-learning algorithms. This is still the stuff of science fiction, and at the same time, available for your desktop right now.
Microsoft, for example, offer an end-to-end solution to deploy and share advanced predictive analytics. It is already making an impact in predictive maintenance, energy demand forecasting, customer profiling, anomaly detection and many other areas.
In telecoms, for instance, Microsoft Azure Machine Learning was employed to predict a telephony network switch failure ahead of time by determining patterns in call drop rates using live and historical call description records.
Self-learning, intelligent systems are rapidly becoming the enterprise weapon of choice. By 2018, according to Gartner, over half of large organisations “will compete using advanced analytics and proprietary algorithms, causing disruption of entire industries.” http://www.gartner.com/newsroom/id/3192717
Agencies need to make tough calls on where to place their bets. The marketplace is evolving fast and funds are limited. The challenge is to keep evolving and adapting without losing their core agency DNA. This applies to the day-to-day processes just as much as longer-term strategic decisions.
Delivering a campaign can range from a relatively simple, linear project to one involving multiple creative inputs, production variables, timelines and resources. The more complex a project gets, the more likely it is to end up a fire-fight to keep it on track.
One thing predictive intelligence can do is recognise potential issues before they become a problem, and then resolve them automatically.
Let’s look at a possible scenario. Using machine-learning algorithms applied to all past projects, the system flags a potential resourcing issue – let’s say, a piece of artwork won’t be completed on time. The system calculates the extra resources required and using the agency’s integrated HR function will choose the most appropriate allocation, based on skill-set, experience, availability, cost effectiveness etc. If the best option is to hire a freelancer, the system will automatically book that resource, and with speech and text perceptual intelligence can even take further action on “Sorry, I’ve got too much on right now” replies.
So when the account executives are alerted to the issue they will not be notified of a problem, but of a potential problem that has been avoided. Of course, the process is open to manual intervention and approval at all points.
Multiply this example scenario by the dozens, if not hundreds, of potential issues in a complex campaign and you can see the considerable savings to be made in time, energy, expense and heartache.
And this is only the beginning. Because the system is continually learning, its capabilities expand and calculations get more accurate over time.
My prediction is that over the next few years intelligent systems will be employed in most agencies to provide actionable recommendations and inform management decisions and processes across the entire business, from hiring and keeping talent to figuring out new revenue models.
Survival of the adaptable
We all know well the business visionaries, like Henry Ford and Steve Jobs, who led their companies through the rapid technological changes of the time. True visionaries are rare (as evidenced by the average company lifespan), but with the pace of disruption increasing and the time available for adaption falling, the new visionaries, leading their businesses into the future, will be the systems themselves.
Implementing a new ERP system in an organisation is always a challenge. Users question whether they will be able to cope with analysing the data flow in an integrated system without external support. The accountant wonders whether all the set-up accounts are correct. The chief technologist cannot get rid of troublesome thoughts about whether the routings and boms are set up optimally. This is why it is so important to have a reliable service team to help the company control the system in the post-implementation stage. In this article, we take a look at the issues surrounding the implementation of an ERP system and the benefits of maintaining proactive service support.
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